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300 |
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
|
https://openreview.net/forum?id=jbafwTkVUn
|
[
"Shiyu Wang",
"Mariam Avagyan",
"Yihan Shen",
"Arnaud Lamy",
"Tingran Wang",
"Szabolcs Marka",
"Zsuzsanna Marka",
"John Wright"
] |
Poster
|
Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in data. Existing denoising methods, however, either rely on local approximations that require a linear scan of the entire dataset or treat denoising as generic function approximation problems, sacrificing efficiency and interpretability. We consider the problem of efficiently denoising a new noisy data point sampled from an unknown manifold $\mathcal M \in \mathbb{R}^D$, using only noisy samples. This work proposes a framework for test-time efficient manifold denoising, by framing the concept of "learning-to-denoise" as *"learning-to-optimize"*. We have two technical innovations: (i) *online learning* methods which learn to optimize over the manifold of clean signals using only noisy data, effectively "growing" an optimizer one sample at a time. (ii) *mixed-order* methods which guarantee that the learned optimizers achieve global optimality, ensuring both efficiency and near-optimal denoising performance. We corroborate these claims with theoretical analyses of both the complexity and denoising performance of mixed-order traversal. Our experiments on scientific manifolds demonstrate significantly improved complexity-performance tradeoffs compared to nearest neighbor search, which underpins existing provable denoising approaches based on exhaustive search.
|
Manifold Denoising, Learning-to-optimize
| null | 14,822 |
2502.16819
|
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] | null |
301 |
Graph World Model
|
https://openreview.net/forum?id=xjTrTlBbrc
|
[
"Tao Feng",
"Yexin Wu",
"Guanyu Lin",
"Jiaxuan You"
] |
Poster
|
World models (WMs) demonstrate strong capabilities in prediction, generation, and planning tasks.
Existing WMs primarily focus on unstructured data while cannot leverage the ubiquitous structured data, often represented as graphs, in the digital world. While multiple graph foundation models have been proposed, they focus on graph learning tasks and cannot extend to diverse multi-modal data and interdisciplinary tasks. To address these challenges, we propose the Graph World Model (GWM), a world model that supports both unstructured and graph-structured states with multi-modal information and represents diverse tasks as actions. The core of a GWM is a generic message-passing algorithm to aggregate structured information, either over a unified multi-modal token space by converting multi-modal data into text (GWM-T) or a unified multi-modal embedding space by modality-specific encoders (GWM-E). Notably, GWM introduces action nodes to support diverse tasks, where action nodes are linked to other nodes via direct reference or similarity computation. Extensive experiments on 6 tasks from diverse domains, including multi-modal generation and matching, recommendation, graph prediction, multi-agent, retrieval-augmented generation, and planning and optimization, show that the same GWM outperforms or matches domain-specific baselines' performance, benefits from multi-hop structures, and demonstrate strong zero-shot/few-shot capabilities on unseen new tasks. Our codes for GWM is released at https://github.com/ulab-uiuc/GWM.
|
Graph world model, Unstructured data, Multi-modal information, Zero-shot/few-shot capabilities
|
The Graph World Model (GWM) efficiently integrates unstructured and graph-structured multi-modal data, enhancing diverse tasks through a unified framework.
| 14,809 |
2507.10539
|
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] |
https://github.com/ulab-uiuc/GWM
|
302 |
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
|
https://openreview.net/forum?id=W9s817KqYf
|
[
"Rogerio Bonatti",
"Dan Zhao",
"Francesco Bonacci",
"Dillon Dupont",
"Sara Abdali",
"Yinheng Li",
"Yadong Lu",
"Justin Wagle",
"Kazuhito Koishida",
"Arthur Bucker",
"Lawrence Keunho Jang",
"Zheng Hui"
] |
Poster
|
Large language models (LLMs) show potential as computer agents, enhancing productivity and software accessibility in multi-modal tasks.
However, measuring agent performance in sufficiently realistic and complex environments becomes increasingly challenging as:
(i) most benchmarks are limited to specific modalities/domains (e.g., text-only, web navigation, Q&A) and
(ii) full benchmark evaluations are slow (on order of magnitude of multiple hours/days) given the multi-step sequential nature of tasks.
To address these challenges, we introduce Windows Agent Arena: a general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real OS to use the same applications and tools available to human users when performing tasks.
We create 150+ diverse tasks across representative domains that require agentic abilities in planning, screen understanding, and tool usage.
Our benchmark is scalable and can be seamlessly parallelized for a full benchmark evaluation in as little as $20$ minutes.
Our work not only speeds up the development and evaluation cycle of multi-modal agents, but also highlights and analyzes existing shortfalls in the agentic abilities of several multimodal LLMs as agents within the Windows computing environment---with the best achieving only a 19.5\% success rate compared to a human success rate of 74.5\%.
|
agents, benchmark, computer agents, AI agents, multimodal agents, large language models
|
A scalable, eficient, and fast open-sourced framework to test mutlimodal AI agents on PC within a full Windows OS environment
| 14,807 |
2409.08264
|
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] |
https://github.com/microsoft/WindowsAgentArena
|
303 |
Understanding Mode Connectivity via Parameter Space Symmetry
|
https://openreview.net/forum?id=E8dMQGsKZv
|
[
"Bo Zhao",
"Nima Dehmamy",
"Robin Walters",
"Rose Yu"
] |
Poster
|
Neural network minima are often connected by curves along which train and test loss remain nearly constant, a phenomenon known as mode connectivity. While this property has enabled applications such as model merging and fine-tuning, its theoretical explanation remains unclear. We propose a new approach to exploring the connectedness of minima using parameter space symmetry. By linking the topology of symmetry groups to that of the minima, we derive the number of connected components of the minima of linear networks and show that skip connections reduce this number. We then examine when mode connectivity and linear mode connectivity hold or fail, using parameter symmetries which account for a significant part of the minimum. Finally, we provide explicit expressions for connecting curves in the minima induced by symmetry. Using the curvature of these curves, we derive conditions under which linear mode connectivity approximately holds. Our findings highlight the role of continuous symmetries in understanding the neural network loss landscape.
|
symmetry, mode connectivity
|
We study why and when mode connectivity holds by linking topological properties of symmetry groups to those of the minimum.
| 14,800 |
2505.23681
|
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] | null |
304 |
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning
|
https://openreview.net/forum?id=TiYOHdK35L
|
[
"Mahavir Dabas",
"Si Chen",
"Charles Fleming",
"Ming Jin",
"Ruoxi Jia"
] |
Poster
|
Safety alignment is crucial for Large Language Models (LLMs) to resist malicious instructions but often results in over-refusals, where benign prompts are unnecessarily rejected, impairing user experience and model utility. To this end, we introduce **ACTOR** (Activation-Based Training for Over-Refusal Reduction), a robust and compute- and-data efficient training framework that mini- mizes over-refusals by utilizing internal activation patterns from diverse queries. ACTOR precisely identifies and adjusts the activation components that trigger refusals, providing stronger control over the refusal mechanism. By fine-tuning only a single model layer, ACTOR effectively reduces over-refusals across multiple benchmarks while maintaining the model’s ability to handle harmful queries and preserving overall utility.
|
LLM, Alignment, Over-refusal
| null | 14,799 |
2507.04250
|
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] | null |
305 |
Test-Time Training Provably Improves Transformers as In-context Learners
|
https://openreview.net/forum?id=bma2FB5MNs
|
[
"Halil Alperen Gozeten",
"Muhammed Emrullah Ildiz",
"Xuechen Zhang",
"Mahdi Soltanolkotabi",
"Marco Mondelli",
"Samet Oymak"
] |
Poster
|
Test-time training (TTT) methods explicitly update the weights of a model to adapt to the specific test instance, and they have found success in a variety of settings, including most recently language modeling and reasoning. To demystify this success, we investigate a gradient-based TTT algorithm for in-context learning, where we train a transformer model on the in-context demonstrations provided in the test prompt. Specifically, we provide a comprehensive theoretical characterization of linear transformers when the update rule is a single gradient step. Our theory (i) delineates the role of alignment between pretraining distribution and target task, (ii) demystifies how TTT can alleviate distribution shift, and (iii) quantifies the sample complexity of TTT including how it can significantly reduce the eventual sample size required for in-context learning. As our empirical contribution, we study the benefits of TTT for TabPFN, a tabular foundation model. In line with our theory, we demonstrate that TTT significantly reduces the required sample size for tabular classification (3 to 5 times fewer) unlocking substantial inference efficiency with a negligible training cost.
|
in-context learning, transformers, test-time training, gradient descent
|
We characterize the theoretical benefits of test-time training for in-context learning and corroborate its benefits for tabular foundation models.
| 14,794 |
2503.11842
|
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] | null |
306 |
Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data
|
https://openreview.net/forum?id=k4KVhQd19x
|
[
"Sayed Mohammad Hosseini",
"Maryam Shanechi"
] |
Poster
|
High-dimensional imaging of neural activity, such as widefield calcium and functional ultrasound imaging, provide a rich source of information for understanding the relationship between brain activity and behavior. Accurately modeling neural dynamics in these modalities is crucial for understanding this relationship but is hindered by the high-dimensionality, complex spatiotemporal dependencies, and prevalent behaviorally irrelevant dynamics in these modalities. Existing dynamical models often employ preprocessing steps to obtain low-dimensional representations from neural image modalities. However, this process can discard behaviorally relevant information and miss spatiotemporal structure. We propose SBIND, a novel data-driven deep learning framework to model spatiotemporal dependencies in neural images and disentangle their behaviorally relevant dynamics from other neural dynamics. We validate SBIND on widefield imaging datasets, and show its extension to functional ultrasound imaging, a recent modality whose dynamical modeling has largely remained unexplored. We find that our model effectively identifies both local and long-range spatial dependencies across the brain while also dissociating behaviorally relevant neural dynamics. Doing so, SBIND outperforms existing models in neural-behavioral prediction. Overall, SBIND provides a versatile tool for investigating the neural mechanisms underlying behavior using imaging modalities.
|
Deep Learning, Dynamical Modeling, Neural Imaging, Behavior, Neuroscience
|
We propose a method to model spatiotemporal dynamics in neural imaging data and their relationship to behavior.
| 14,793 | null |
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] |
https://github.com/ShanechiLab/SBIND
|
307 |
ELoRA: Low-Rank Adaptation for Equivariant GNNs
|
https://openreview.net/forum?id=hcoxm3Vwgy
|
[
"Chen Wang",
"Siyu Hu",
"Guangming Tan",
"Weile Jia"
] |
Poster
|
Pre-trained interatomic potentials have become a new paradigm for atomistic materials simulations, enabling accurate and efficient predictions across diverse chemical systems. Despite their promise, fine-tuning is often required for complex tasks to achieve high accuracy. Traditional parameter-efficient fine-tuning approaches are effective in NLP and CV. However, when applied to SO(3) equivariant pre-trained interatomic potentials, these methods will inevitably break equivariance—a critical property for preserving physical symmetries. In this paper, we introduce ELoRA (Equivariant Low-Rank Adaptation), a novel fine-tuning method designed specifically for SO(3) equivariant Graph Neural Networks (GNNs), the backbones in multiple pre-trained interatomic potentials. ELoRA adopts a path-dependent decomposition for weights updating which offers two key advantages: (1) it preserves SO(3) equivariance throughout the fine-tuning process, ensuring physically consistent predictions, and (2) it leverages low-rank adaptations to significantly improve data efficiency. We prove that ELoRA maintains equivariance and demonstrate its effectiveness through comprehensive experiments. On the rMD17 organic dataset, ELoRA achieves a 25.5\% improvement in energy prediction accuracy and a 23.7\% improvement in force prediction accuracy compared to full-parameter fine-tuning. Similarly, across 10 inorganic datasets, ELoRA achieves average improvements of 12.3\% and 14.4\% in energy and force predictions, respectively. Code will be made publicly available at https://github.com/hyjwpk/ELoRA.
|
graph neural network, equivariance, parameter-efficient fine-tuning, low-rank adaptation, interatomic potential
| null | 14,791 | null |
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] |
https://github.com/hyjwpk/ELoRA
|
308 |
Tracking Most Significant Shifts in Infinite-Armed Bandits
|
https://openreview.net/forum?id=CP2eFH3Ex0
|
[
"Joe Suk",
"Jung-hun Kim"
] |
Poster
|
We study an infinite-armed bandit problem where actions' mean rewards are initially sampled from a _reservoir distribution_. Most prior works in this setting focused on stationary rewards (Berry et al., 1997; Wang et al., 2008; Bonald and Proutiere, 2013; Carpentier and Valko, 2015) with the more challenging adversarial/non-stationary variant only recently studied in the context of rotting/decreasing rewards (Kim et al., 2022; 2024). Furthermore, optimal regret upper bounds were only achieved using parameter knowledge of non-stationarity and only known for certain regimes of regularity of the reservoir. This work shows the first parameter-free optimal regret bounds while also relaxing these distributional assumptions. We also study a natural notion of _significant shift_ for this problem inspired by recent developments in finite-armed MAB (Suk & Kpotufe, 2022). We show that tighter regret bounds in terms of significant shifts can be adaptively attained. Our enhanced rates only depend on the rotting non-stationarity and thus exhibit an interesting phenomenon for this problem where rising non-stationarity does not factor into the difficulty of non-stationarity.
|
non-stationary, infinite-armed bandits, bandits, adaptive, parameter-free
|
First optimal and adaptive regret bounds for non-stationary infinite-armed bandits
| 14,788 |
2502.00108
|
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] |
https://github.com/joesuk/NonStationaryInfiniteBandits
|
309 |
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
|
https://openreview.net/forum?id=J3gzdbYZxS
|
[
"Ryan Liu",
"Jiayi Geng",
"Addison J. Wu",
"Ilia Sucholutsky",
"Tania Lombrozo",
"Thomas L. Griffiths"
] |
Poster
|
Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance.
However, it is still an open question under which settings CoT systematically reduces performance. In this paper, we seek to identify the characteristics of tasks where CoT reduces performance by drawing inspiration from cognitive psychology, focusing on six representative tasks from the psychological literature where deliberation hurts performance in humans. In three of these tasks, state-of-the-art models exhibit significant performance drop-offs with CoT (up to 36.3\% absolute accuracy for OpenAI o1-preview compared to GPT-4o), while in others, CoT effects are mixed, with positive, neutral, and negative changes. While models and humans do not exhibit perfectly parallel cognitive processes, considering cases where thinking has negative consequences for humans helps identify settings where it negatively impacts models. By connecting the literature on human verbal thinking and deliberation with evaluations of CoT, we offer a perspective for understanding the impact of inference-time reasoning.
|
chain of thought, psychology, cognitive science
|
We compare human verbal thinking with model chain-of-thought.
| 14,780 |
2410.21333
|
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] |
https://github.com/JiayiGeng/CoT_overthinking
|
310 |
Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions
|
https://openreview.net/forum?id=NBtgS3OJh4
|
[
"Geonho Hwang",
"Yeachan Park",
"Wonyeol Lee",
"Sejun Park"
] |
Poster
|
Existing works on the expressive power of neural networks typically assume real-valued parameters and exact mathematical operations during the evaluation of networks. However, neural networks run on actual computers can take parameters only from a small subset of the reals and perform inexact mathematical operations with round-off errors and overflows. In this work, we study the expressive power of floating-point neural networks, i.e., networks with floating-point parameters and operations. We first observe that for floating-point neural networks to represent all functions from floating-point vectors to floating-point vectors, it is necessary to distinguish different inputs: the first layer of a network should be able to generate different outputs for different inputs. We also prove that such distinguishability is sufficient, along with mild conditions on activation functions. Our result shows that with practical activation functions, floating-point neural networks can represent floating-point functions from a wide domain to all finite or infinite floats. For example, the domain is all finite floats for Sigmoid and tanh, and it is all finite floats of magnitude less than 1/8 times the largest float for ReLU, ELU, SeLU, GELU, Swish, Mish, and sin.
|
Universal approximation, Floating-Point Arithmetic
| null | 14,776 | null |
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] | null |
311 |
Geometry Informed Tokenization of Molecules for Language Model Generation
|
https://openreview.net/forum?id=4umRQdvuW5
|
[
"Xiner Li",
"Limei Wang",
"Youzhi Luo",
"Carl Edwards",
"Shurui Gui",
"Yuchao Lin",
"Heng Ji",
"Shuiwang Ji"
] |
Poster
|
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored. Here, we attempt to bridge this gap by proposing a novel method which converts molecular geometries into SE(3)-invariant 1D discrete sequences. Our method consists of canonical labeling and invariant spherical representation steps, which together maintain geometric and atomic fidelity in a format conducive to LMs. Our experiments show that, when coupled with our proposed method, various LMs excel in molecular geometry generation, especially in controlled generation tasks. Our code has been released as part of the AIRS library (https://github.com/divelab/AIRS/).
|
Language models
| null | 14,772 |
2408.10120
|
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] |
https://github.com/divelab/AIRS/tree/main/OpenMol/Geo2Seq
|
312 |
Uncertainty Quantification for LLM-Based Survey Simulations
|
https://openreview.net/forum?id=nY1Ge2wxtP
|
[
"Chengpiao Huang",
"Yuhang Wu",
"Kaizheng Wang"
] |
Poster
|
We investigate the use of large language models (LLMs) to simulate human responses to survey questions, and perform uncertainty quantification to gain reliable insights. Our approach converts imperfect, LLM-simulated responses into confidence sets for population parameters of human responses, addressing the distribution shift between the simulated and real populations. A key innovation lies in determining the optimal number of simulated responses: too many produce overly narrow confidence sets with poor coverage, while too few yield excessively loose estimates. To resolve this, our method adaptively selects the simulation sample size, ensuring valid average-case coverage guarantees. It is broadly applicable to any LLM, irrespective of its fidelity, and any procedure for constructing confidence sets. Additionally, the selected sample size quantifies the degree of misalignment between the LLM and the target human population. We illustrate our method on real datasets and LLMs.
|
synthetic data, large language models, uncertainty quantification, simulation
|
Uncertainty Quantification for LLM-Based Survey Simulations
| 14,770 |
2502.17773
|
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] |
https://github.com/yw3453/uq-llm-survey-simulation
|
313 |
(How) Can Transformers Predict Pseudo-Random Numbers?
|
https://openreview.net/forum?id=asDx9sPAUN
|
[
"Tao Tao",
"Darshil Doshi",
"Dayal Singh Kalra",
"Tianyu He",
"Maissam Barkeshli"
] |
Poster
|
Transformers excel at discovering patterns in sequential data, yet their fundamental limitations and learning mechanisms remain crucial topics of investigation. In this paper, we study the ability of Transformers to learn pseudo-random number sequences from linear congruential generators (LCGs), defined by the recurrence relation $x_{t+1} = a x_t + c \\;\mathrm{mod}\\; m$. We find that with sufficient architectural capacity and training data variety, Transformers can perform in-context prediction of LCG sequences with unseen moduli ($m$) and parameters ($a,c$). By analyzing the embedding layers and attention patterns, we uncover how Transformers develop algorithmic structures to learn these sequences in two scenarios of increasing complexity. First, we investigate how Transformers learn LCG sequences with unseen ($a, c$) but fixed modulus; and demonstrate successful learning up to $m = 2^{32}$.
We find that models learn to factorize $m$ and utilize digit-wise number representations to make sequential predictions. In the second, more challenging scenario of unseen moduli, we show that Transformers can generalize to unseen moduli up to $m_{\text{test}} = 2^{16}$. In this case, the model employs a two-step strategy: first estimating the unknown modulus from the context, then utilizing prime factorizations to generate predictions. For this task, we observe a sharp transition in the accuracy at a critical depth $d= 3$. We also find that the number of in-context sequence elements needed to reach high accuracy scales sublinearly with the modulus.
|
Interpretability, In-context learning, Grokking, Transformer, Pseudo-random number generators
|
We show that transformers can learn to predict sequences from Linear Congruential Generators. We identify the underlying algorithm employed by trained models, which involves estimating the modulus in-context and finding its prime factorization.
| 14,764 |
2502.10390
|
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] |
https://github.com/dayal-kalra/transformer-prng
|
314 |
Causal Abstraction Inference under Lossy Representations
|
https://openreview.net/forum?id=WDybFnCPaB
|
[
"Kevin Muyuan Xia",
"Elias Bareinboim"
] |
Poster
|
The study of causal abstractions bridges two integral components of human intelligence: the ability to determine cause and effect, and the ability to interpret complex patterns into abstract concepts. Formally, causal abstraction frameworks define connections between complicated low-level causal models and simple high-level ones. One major limitation of most existing definitions is that they are not well-defined when considering lossy abstraction functions in which multiple low-level interventions can have different effects while mapping to the same high-level intervention (an assumption called the abstract invariance condition). In this paper, we introduce a new type of abstractions called projected abstractions that generalize existing definitions to accommodate lossy representations. We show how to construct a projected abstraction from the low-level model and how it translates equivalent observational, interventional, and counterfactual causal queries from low to high-level. Given that the true model is rarely available in practice we prove a new graphical criteria for identifying and estimating high-level causal queries from limited low-level data. Finally, we experimentally show the effectiveness of projected abstraction models in high-dimensional image settings.
|
causality, causal inference, causal abstractions, neural networks, deep learning, representation learning, neural causal models
|
We generalize causal abstraction models to handle cases where high-level interventions may be ambiguous, and this allows for practical causal inferences in high-dimensional settings.
| 14,754 | null |
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] | null |
315 |
Safety-Polarized and Prioritized Reinforcement Learning
|
https://openreview.net/forum?id=x10ryC8F0C
|
[
"Ke Fan",
"Jinpeng Zhang",
"Xuefeng Zhang",
"Yunze Wu",
"Jingyu Cao",
"Yuan Zhou",
"Jianzhu Ma"
] |
Poster
|
Motivated by the first priority of safety in many real-world applications, we propose \textsc{MaxSafe}, a chance-constrained bi-level optimization framework for safe reinforcement learning. \textsc{MaxSafe} first minimizes the unsafe probability and then maximizes the return among the safest policies. We provide a tailored Q-learning algorithm for the \textsc{MaxSafe} objective, featuring a novel learning process for \emph{optimal action masks} with theoretical convergence guarantees. To enable the application of our algorithm to large-scale experiments, we introduce two key techniques: \emph{safety polarization} and \emph{safety prioritized experience replay}. Safety polarization generalizes the optimal action masking by polarizing the Q-function, which assigns low values to unsafe state-action pairs, effectively discouraging their selection. In parallel, safety prioritized experience replay enhances the learning of optimal action masks by prioritizing samples based on temporal-difference (TD) errors derived from our proposed state-action reachability estimation functions. This approach efficiently addresses the challenges posed by sparse cost signals. Experiments on diverse autonomous driving and safe control tasks show that our methods achieve near-maximal safety and an optimal reward-safety trade-off.
|
Safe Reinforcement Learning
| null | 14,741 | null |
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] |
https://github.com/FrankSinatral/Safety-PP
|
316 |
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
|
https://openreview.net/forum?id=xl9sv9vEDy
|
[
"Honghua Dong",
"Jiacheng Yang",
"Xun Deng",
"Yuhe Jiang",
"Gennady Pekhimenko",
"Fan Long",
"Xujie Si"
] |
Poster
|
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, their type inference capabilities remain underexplored. We introduce `TypyBench`, a benchmark designed to evaluate LLMs' type inference across entire Python repositories. `TypyBench` features two novel metrics: `TypeSim`, which captures nuanced semantic relationships between predicted and ground truth types, and `TypeCheck`, which assesses type consistency across codebases. Our evaluation of various LLMs on a curated dataset of 50 high-quality Python repositories reveals that, although LLMs achieve decent `TypeSim` scores, they struggle with complex nested types and exhibit significant type consistency errors. These findings suggest that future research should shift focus from improving type similarity to addressing repository-level consistency. `TypyBench` provides a foundation for this new direction, offering insights into model performance across different type complexities and usage contexts. Our code and data are available at \href{https://github.com/typybench/typybench}.
|
Benchmark, Type Inference, Untyped Python Repo, Large Language Models, Long-Context, Repo-Level, Software Engineering
|
We introduce TypyBench to evaluate LLMs' Python type inference, with new metrics for type similarity and consistency. While LLMs achieve good performance on individual types, most of them struggle with complex types and cross-file consistency.
| 14,734 | null |
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] |
https://github.com/typybench/typybench
|
317 |
On Exact Bit-level Reversible Transformers Without Changing Architecture
|
https://openreview.net/forum?id=wHaL4GkvOP
|
[
"Guoqiang Zhang",
"JP Lewis",
"W. Bastiaan Kleijn"
] |
Poster
|
In this work we present the BDIA-transformer, which is an exact bit-level reversible transformer that uses an unchanged standard architecture for inference. The basic idea is to first treat each transformer block as the Euler integration approximation for solving an ordinary differential equation (ODE) and then incorporate the technique of bidirectional integration approximation (BDIA) (originally designed for diffusion inversion) into the neural architecture, together with activation quantization to make it exactly bit-level reversible. In the training process, we let a hyper-parameter $\gamma$ in BDIA-transformer randomly take one of the two values $\{0.5, -0.5\}$ per training sample per transformer block for averaging every two consecutive integration approximations. As a result, BDIA-transformer can be viewed as training an ensemble of ODE solvers parameterized by a set of binary random variables, which regularizes the model and results in improved validation accuracy. Lightweight side information is required to be stored in the forward process to account for binary quantization loss to enable exact bit-level reversibility. In the inference procedure, the expectation $\mathbb{E}(\gamma)=0$ is taken to make the resulting architecture identical to transformer up to activation quantization. Our experiments in natural language generation, image classification, and language translation show that BDIA-transformers outperform their conventional counterparts significantly in terms of validation performance while also requiring considerably less training memory. Thanks to the regularizing effect of the ensemble, the BDIA-transformer is particularly suitable for fine-tuning with limited data. Source-code can be found via \href{https://github.com/guoqiang-zhang-x/BDIA-Transformer}{this link}.
|
transformer; reversability; BDIA; quantization
|
A new type of reversible transformer for online back-propagation that improves generalization performance and becomes the standard transformer in inference procedure
| 14,733 |
2407.09093
|
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] |
https://github.com/guoqiang-zhang-x/BDIA-Transformer
|
318 |
Reinforcement Learning Control of a Physical Robot Device for Assisted Human Walking without a Simulator
|
https://openreview.net/forum?id=yAdcCADXqH
|
[
"Junmin Zhong",
"Emiliano Quinones Yumbla",
"Seyed Yousef Soltanian",
"Ruofan Wu",
"Wenlong Zhang",
"Jennie Si"
] |
Poster
|
This study presents an innovative reinforcement learning (RL) control approach to facilitate soft exosuit-assisted human walking. Our goal is to address the ongoing challenges in developing reliable RL-based methods for controlling physical devices. To overcome key obstacles—such as limited data, the absence of a simulator for human-robot interaction during walking, the need for low computational overhead in real-time deployment, and the demand for rapid adaptation to achieve personalized control while ensuring human safety—we propose an online Adaptation from an offline Imitating Expert Policy (AIP) approach. Our offline learning mimics human expert actions through real human walking demonstrations without robot assistance. The resulted policy is then used to initialize online actor-critic learning, the goal of which is to optimally personalize robot assistance. In addition to being fast and robust, our online RL method also posses important properties such as learning convergence, dynamic stability, and solution optimality. We have successfully demonstrated our simple
and robust framework for safe robot control on all five tested human participants, without selectively presenting results. The qualitative performance guarantees provided by our online RL, along with the consistent experimental validation of AIP control, represent the first demonstration of online adaptation for softsuit control personalization and serve as important evidence for the use of online RL in controlling a physical device to solve a real-life problem.
|
Soft Robotics, Human robotic interaction, reinforcement learning without simulator
| null | 14,708 | null |
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] |
https://github.com/JennieSi-Lab-RLOC/ICML2025-AIP
|
319 |
Heterogeneous Sufficient Dimension Reduction and Subspace Clustering
|
https://openreview.net/forum?id=t9RKslSC00
|
[
"Lei Yan",
"Xin Zhang",
"Qing Mai"
] |
Poster
|
Scientific and engineering applications are often heterogeneous, making it beneficial to account for latent clusters or sub-populations when learning low-dimensional subspaces in supervised learning, and vice versa. In this paper, we combine the concept of subspace clustering with model-based sufficient dimension reduction and thus generalize the sufficient dimension reduction framework from homogeneous regression setting to heterogeneous data applications. In particular, we propose the mixture of principal fitted components (mixPFC) model, a novel framework that simultaneously achieves clustering, subspace estimation, and variable selection, providing a unified solution for high-dimensional heterogeneous data analysis. We develop a group Lasso penalized expectation-maximization (EM) algorithm and obtain its non-asymptotic convergence rate. Through extensive simulation studies, mixPFC demonstrates superior performance compared to existing methods across various settings. Applications to real world datasets further highlight its effectiveness and practical advantages.
|
High-dimensional inference, EM algorithm, Mixture model
|
The paper presents mixPFC, a model combining subspace clustering with sufficient dimension reduction for heterogeneous data, achieving clustering, subspace estimation, and variable selection with theoretical guarantees.
| 14,682 | null |
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] |
https://github.com/leiyan-ly/mixPFC
|
320 |
Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
|
https://openreview.net/forum?id=5g6LPR0Dlx
|
[
"Harit Vishwakarma",
"Alan Mishler",
"Thomas Cook",
"Niccolo Dalmasso",
"Natraj Raman",
"Sumitra Ganesh"
] |
Poster
|
Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect outputs pose significant risks in high-stakes domains like healthcare and finance. To quantify LLM uncertainty and thereby mitigate these risks, recent works employ conformal prediction (CP), a model- and distribution-agnostic framework that uses LLM outputs to generate a \emph{prediction set} containing the true answer with high probability. Leveraging CP, we propose \emph{conformal revision of questions} (CROQ), which revises the question by narrowing down the available choices to those in the prediction set and asking the LLM the revised question. We expect LLMs to be more accurate on revised questions with fewer choices. Furthermore, we expect CROQ to be effective when the prediction sets from CP are small. Commonly used logit scores often lead to large sets, diminishing CROQ's effectiveness. To overcome this, we propose CP-OPT, an optimization framework to learn scores that minimize set sizes while maintaining coverage. Our extensive experiments on MMLU, ToolAlpaca, and TruthfulQA datasets with multiple LLMs show that CROQ improves accuracy over the standard inference, with more pronounced gains when paired with CP-OPT.
|
Large Language Models, Conformal Prediction, Uncertainty Quantification, Prompting, MCQ, Tool Learning, Agentic AI, Test-time Scaling
|
We propose CROQ, which improves LLM accuracy by refining MCQ answer choices using conformal prediction. To enhance CROQ, we introduce CP-OPT, optimizing scores for smaller prediction sets. Experiments show CROQ is effective, especially with CP-OPT.
| 14,662 |
2501.00555
|
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] | null |
321 |
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
|
https://openreview.net/forum?id=45he3Ri6JP
|
[
"Runquan Gui",
"Zhihai Wang",
"Jie Wang",
"Chi Ma",
"Huiling Zhen",
"Mingxuan Yuan",
"Jianye HAO",
"Defu Lian",
"Enhong Chen",
"Feng Wu"
] |
Poster
|
Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning.
However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks.
To address these challenges, we propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.
The hypertree structure enables LLMs to engage in hierarchical thinking by flexibly employing the divide-and-conquer strategy, effectively breaking down intricate reasoning steps, accommodating diverse constraints, and managing multiple distinct sub-tasks in a well-organized manner.
We further introduce an autonomous planning framework that completes the planning process by iteratively refining and expanding the hypertree-structured planning outlines.
Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6$\times$ performance improvement over o1-preview.
|
LLM Reasoning, Planning, Hierarchical Thinking
| null | 14,660 |
2505.02322
|
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] | null |
322 |
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
|
https://openreview.net/forum?id=NME3HKUHLX
|
[
"Han Zhong",
"Yutong Yin",
"Shenao Zhang",
"Xiaojun Xu",
"Yuanxin Liu",
"Yifei Zuo",
"Zhihan Liu",
"Boyi Liu",
"Sirui Zheng",
"Hongyi Guo",
"Liwei Wang",
"Mingyi Hong",
"Zhaoran Wang"
] |
Poster
|
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, yet generating reliable reasoning processes remains a significant challenge. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model incorporating latent thinking processes and evaluation signals. Our framework addresses two critical questions: (1) how to generate high-quality reasoning processes during inference automatically, and (2) how to integrate these processes into post-training. We propose the \emph{Bootstrapping Reinforced Thinking Process} (BRiTE) algorithm and demonstrate its theoretical convergence at a rate of $1/T$, where $T$ is the number of iterations. The algorithm operates in two steps. First, it generates high-quality rationales by approximating the desired posterior distribution using a reinforcement learning approach with a novel reward shaping mechanism. Second, it fine-tunes the base LLM by maximizing the joint probability of rationale generation with respect to LLM parameters. Empirical evaluation on GSM8K and MATH benchmarks demonstrates that our approach consistently improves performance across different model sizes without requiring human-annotated thinking processes, outperforming standard chain-of-thought prompting while enhancing existing post-training methods.
|
reasoning, boostrapping, RL, rational generation
| null | 14,659 |
2501.18858
|
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] | null |
323 |
Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph
|
https://openreview.net/forum?id=skoBTs4ke4
|
[
"Zhiqiang Wang",
"Jianghao Wen",
"Jianqing Liang"
] |
Poster
|
Dynamic graph representation learning using Spiking Neural Networks (SNNs) exploits the temporal spiking behavior of neurons, offering advantages in capturing the temporal evolution and sparsity of dynamic graphs. However, existing SNN-based methods often fail to effectively capture the impact of latency in information propagation on node representations. To address this, we propose Delay-DSGN, a dynamic spiking graph neural network incorporating a learnable delay mechanism. By leveraging synaptic plasticity, the model dynamically adjusts connection weights and propagation speeds, enhancing temporal correlations and enabling historical data to influence future representations. Specifically, we introduce a Gaussian delay kernel into the neighborhood aggregation process at each time step, adaptively delaying historical information to future time steps and mitigating information forgetting. Experiments on three large-scale dynamic graph datasets demonstrate that Delay-DSGN outperforms eight state-of-the-art methods, achieving the best results in node classification tasks. We also theoretically derive the constraint conditions between the Gaussian kernel's standard deviation and size, ensuring stable training and preventing gradient explosion and vanishing issues.
|
Graph Neural Networks, Dynamic Graph, Spiking Neural Network, Graph Representation Learning
|
This work introduces Delay-DSGN, a dynamic spiking graph neural network with a learnable delay mechanism, to accurately capture temporal dependencies and mitigate information forgetting in dynamic graph representation learning.
| 14,657 | null |
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] | null |
324 |
An End-to-End Model for Logits-Based Large Language Models Watermarking
|
https://openreview.net/forum?id=9sNiCqi2RD
|
[
"KA HIM WONG",
"Jicheng Zhou",
"Jiantao Zhou",
"Yain-Whar Si"
] |
Poster
|
The rise of LLMs has increased concerns over source tracing and copyright protection for AIGC, highlighting the need for advanced detection technologies. Passive detection methods usually face high false positives, while active watermarking techniques using logits or sampling manipulation offer more effective protection. Existing LLM watermarking methods, though effective on unaltered content, suffer significant performance drops when the text is modified and could introduce biases that degrade LLM performance in downstream tasks. These methods fail to achieve an optimal tradeoff between text quality and robustness, particularly due to the lack of end-to-end optimization of the encoder and decoder. In this paper, we introduce a novel end-to-end logits perturbation method for watermarking LLM-generated text. By joint optimization, our approach achieves a better balance between quality and robustness. To address non-differentiable operations in the end-to-end training pipeline, we introduce an online-prompting technique that leverages the on-the-fly LLM as a differentiable surrogate. Our method achieves superior robustness, outperforming distortion-free methods by 37–39% under paraphrasing and 17.2% on average, while maintaining text quality on par with the distortion-free methods in terms of text perplexity and downstream tasks. Our method can be easily generalized to different LLMs. Code is available at https://github.com/KAHIMWONG/E2E_LLM_WM.
|
LLM watermarking, End-to-end optimization, Robustness
|
We introduce the first logits-based end-to-end model for LLM watermarking, where encoder and decoder networks are jointly optimized to improve detection robustness and text quality.
| 14,656 |
2505.02344
|
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] |
https://github.com/KAHIMWONG/E2E_LLM_WM
|
325 |
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
|
https://openreview.net/forum?id=XkEp70qckE
|
[
"Wei Liu",
"Zhongyu Niu",
"Lang Gao",
"Zhiying Deng",
"Jun Wang",
"Haozhao Wang",
"Ruixuan Li"
] |
Poster
|
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, and a subsequent predictor utilizes the selected subset for its input. The generator and predictor are trained collaboratively to maximize prediction accuracy. In this paper, we first uncover a potential caveat: such a cooperative game could unintentionally introduce a sampling bias during rationale extraction. Specifically, the generator might inadvertently create an incorrect correlation between the selected rationale candidate and the label, even when they are semantically unrelated in the original dataset. Subsequently, we elucidate the origins of this bias using both detailed theoretical analysis and empirical evidence. Our findings suggest a direction for inspecting these correlations through attacks, based on which we further introduce an instruction to prevent the predictor from learning the correlations.
Through experiments on six text classification datasets and two graph classification datasets using three network architectures (GRUs, BERT, and GCN), we show that our method significantly outperforms recent rationalization methods.
|
Unsupervised rationale extraction, selective rationalization, self-explaining, interpretability
| null | 14,655 |
2505.02118
|
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] |
https://github.com/jugechengzi/Rationalization-A2I
|
326 |
Logarithmic Regret for Online KL-Regularized Reinforcement Learning
|
https://openreview.net/forum?id=6QH9IB53uy
|
[
"Heyang Zhao",
"Chenlu Ye",
"Wei Xiong",
"Quanquan Gu",
"Tong Zhang"
] |
Poster
|
Recent advances in Reinforcement Learning from Human Feedback (RLHF) have shown that KL-regularization plays a pivotal role in improving the efficiency of RL fine-tuning for large language models (LLMs). Despite its empirical advantage, the theoretical difference between KL-regularized RL and standard RL remains largely under-explored. While there is a recent line of work on the theoretical analysis of KL-regularized objective in decision making (Xiong et al., 2024a; Xie et al., 2024; Zhao et al., 2024), these analyses either reduce to the traditional RL setting or rely on strong coverage assumptions. In this paper, we propose an optimism-based KL-regularized online contextual bandit algorithm, and provide a novel analysis of its regret. By carefully leveraging the benign optimization landscape induced by the KL-regularization and the optimistic reward estimation, our algorithm achieves an $\mathcal{O}\big(\eta\log (N_{\mathcal R} T)\cdot d_{\mathcal R}\big)$ logarithmic regret bound, where $\eta, N_{\mathcal R},T,d_{\mathcal R}$ denote the KL-regularization parameter, the cardinality of the reward function class, number of rounds, and the complexity of the reward function class. Furthermore, we extend our algorithm and analysis to reinforcement learning by developing a novel decomposition over transition steps and also obtain a similar logarithmic regret bound.
|
Reinforcement learning, regularization
| null | 14,654 |
2502.07460
|
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] | null |
327 |
EditLord: Learning Code Transformation Rules for Code Editing
|
https://openreview.net/forum?id=CxQPqcxRnx
|
[
"Weichen Li",
"Albert Jan",
"Baishakhi Ray",
"Junfeng Yang",
"Chengzhi Mao",
"Kexin Pei"
] |
Poster
|
Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original code's intended functionality.
Existing approaches often formulate code editing as an implicit end-to-end task, omitting the fact that code-editing procedures inherently consist of discrete and explicit steps, and thus suffer from suboptimal performance and lack of robustness and generalization.
We introduce EditLord, a code editing framework that makes the code transformation steps explicit.
Our key insight is to employ a language model (LM) as an inductive learner to extract code editing rules from the training code pairs as concise meta-rule sets.
Such rule sets will be manifested for each training sample to augment them for finetuning or assist in prompting- and iterative-based code editing.
EditLord outperforms the state-of-the-art by an average of 22.7% in editing performance and 58.1% in robustness while achieving 20.2% higher functional correctness, across critical software engineering and security applications, LM models, and editing modes.
|
Large Language Models, Machine Learning for Code Editing, Code Generation, Reasoning
|
The paper introduces EditLord, a novel framework that enhances LLMs for code editing by introducing discrete and explicit procedural steps, leading to improved performance on various code editing tasks and better robustness.
| 14,642 |
2504.15284
|
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] | null |
328 |
MVA: Linear Attention with High-order Query-Keys Integration and Multi-level Vocabulary Decomposition
|
https://openreview.net/forum?id=WSUO2uRDPc
|
[
"wang ning",
"Zekun Li",
"Tongxin Bai",
"Man Yao",
"Zhen Qin",
"Guoqi Li"
] |
Poster
|
Linear attention offers the advantages of linear inference time and fixed memory usage compared to Softmax attention.
However, training large-scale language models with linear attention from scratch remains prohibitively expensive and exhibits significant performance gaps compared to Softmax-based models.
To address these challenges, we focus on transforming pre-trained Softmax-based language models into linear attention models.
We unify mainstream linear attention methods using a **high-order QK integration theory** and a **multi-level vocabulary decomposition**.
Specifically, the QK integration theory explains the efficacy of combining linear and sparse attention from the perspective of information collection across different frequency bands.
The multi-level vocabulary decomposition exponentially expands memory capacity by recursively exploiting compression loss from compressed states.
Through detailed error analysis, we demonstrate superior approximation of Softmax attention achieved by our approach.
To further improve performance and reduce training costs, we adopt a **soft integration strategy** with attention scores, effectively combining a sliding window mechanism.
With less than 100M tokens, our method fine-tunes models to achieve linear complexity while retaining 99\% of their original performance.
Compared to state-of-the-art linear attention model and method, our approach improves MMLU scores by 1.2 percentage points with minimal fine-tuning.
Furthermore, even without the sliding window mechanism, our method achieves state-of-the-art performance on all test sets with 10B tokens.
|
Fine-tune transformer to RNN; Linear attention; Linear Model;
|
Fine-tune transformer to RNN; Linear attention; Linear Model;
| 14,640 | null |
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] | null |
329 |
Conformal Tail Risk Control for Large Language Model Alignment
|
https://openreview.net/forum?id=H8DkMvWnSQ
|
[
"Catherine Chen",
"Jingyan Shen",
"Zhun Deng",
"Lihua Lei"
] |
Poster
|
Recent developments in large language models (LLMs) have led to their widespread usage for various tasks. The prevalence of LLMs in society implores the assurance on the reliability of their performance. In particular, risk-sensitive applications demand meticulous attention to unexpectedly poor outcomes, i.e., tail events, for instance, toxic answers, humiliating language, and offensive outputs. Due to the costly nature of acquiring human annotations, general-purpose scoring models have been created to automate the process of quantifying these tail events. This phenomenon introduces potential human-machine misalignment between the respective scoring mechanisms. In this work, we present a lightweight calibration framework for blackbox models that ensures the alignment of humans and machines with provable guarantees. Our framework provides a rigorous approach to controlling any distortion risk measure that is characterized by a weighted average of quantiles of the loss incurred by the LLM with high confidence. The theoretical foundation of our method relies on the connection between conformal risk control and a traditional family of statistics, i.e., L-statistics. To demonstrate the utility of our framework, we conduct comprehensive experiments that address the issue of human-machine misalignment.
|
large language models, conformal risk control, human-machine alignment, model calibration, L-statistics
|
We develop a method that leverages conformal risk control and asymptotics of L-statistics to achieve guaranteed tail risk control for any given LLM under no assumptions on the statistical properties and the alignment quality of the LLM.
| 14,637 |
2502.20285
|
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-0.0005496299709193408,
-0.02986154891550541
] |
https://github.com/jy-evangeline/DRC
|
330 |
Online Clustering of Dueling Bandits
|
https://openreview.net/forum?id=EscpGI2XAx
|
[
"Zhiyong Wang",
"Jiahang Sun",
"Mingze Kong",
"Jize Xie",
"Qinghua Hu",
"John C.S. Lui",
"Zhongxiang Dai"
] |
Poster
|
The contextual multi-armed bandit (MAB) is a widely used framework for problems requiring sequential decision-making under uncertainty, such as recommendation systems. In applications involving a large number of users, the performance of contextual MAB can be significantly improved by facilitating collaboration among multiple users. This has been achieved by the clustering of bandits (CB) methods, which adaptively group the users into different clusters and achieve collaboration by allowing the users in the same cluster to share data. However, classical CB algorithms typically rely on numerical reward feedback, which may not be practical in certain real-world applications. For instance, in recommendation systems, it is more realistic and reliable to solicit preference feedback between pairs of recommended items rather than absolute rewards. To address this limitation, we introduce the first "clustering of dueling bandit algorithms" to enable collaborative decision-making based on preference feedback. We propose two novel algorithms: (1) Clustering of Linear Dueling Bandits (COLDB) which models the user reward functions as linear functions of the context vectors, and (2) Clustering of Neural Dueling Bandits (CONDB) which uses a neural network to model complex, non-linear user reward functions. Both algorithms are supported by rigorous theoretical analyses, demonstrating that user collaboration leads to improved regret bounds. Extensive empirical evaluations on synthetic and real-world datasets further validate the effectiveness of our methods, establishing their potential in real-world applications involving multiple users with preference-based feedback.
|
Multi-armed bandits, dueling bandits, clustering of bandits
|
We introduce the first clustering of dueling bandit algorithms for both linear and neural bandits, which provably benefit from cross-user collaboration in the presence of preference feedback.
| 14,633 |
2502.02079
|
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] | null |
331 |
Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
|
https://openreview.net/forum?id=UchFXOIwvA
|
[
"Yilong Song",
"Peijin Li",
"Bin Gao",
"Kun Yuan"
] |
Poster
|
Optimization problems on the Stiefel manifold, ranging from principal component analysis to enhancing neural network robustness, are ubiquitous in machine learning. The Landing algorithm avoids computationally expensive retraction operations on manifolds, making it highly competitive for large-scale problems. This paper extends this method to distributed settings, introducing *EF-Landing*, the first retraction-free and communication-efficient algorithm for distributed stochastic optimization on the Stiefel manifold. By incorporating communication compression and error feedback, EF-Landing ensures convergence and constraint feasibility while significantly reducing communication overhead. We provide sharp convergence guarantees, demonstrating that EF-Landing achieves the same asymptotic linear speedup convergence rate as existing methods without communication compression. Furthermore, our analysis is highly versatile, applying to both deterministic and stochastic settings and encompassing algorithms based on gradient descent or momentum-based gradient descent. We also generalize EF-Landing to operate on block-wise Stiefel manifolds, enabling greater flexibility for structured constraints. Extensive numerical experiments validate our theoretical results.
|
Stiefel manifold, distributed optimization, error feedback, retraction-free
| null | 14,625 |
2506.02879
|
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] | null |
332 |
Contrastive Learning with Simplicial Convolutional Networks for Short-Text Classification
|
https://openreview.net/forum?id=I6UAeNdvFe
|
[
"Huang Liang",
"Benedict Lee",
"Daniel Hui Loong Ng",
"Kelin Xia"
] |
Poster
|
Text classification is a fundamental task in Natural Language Processing (NLP). Short text classification has recently captured much attention due to its increased amount from various sources with limited labels and its inherent challenges for its sparsity in words and semantics. Recent studies have adopted self-supervised contrastive learning across different representations to improve performance. However, most of the current models face several challenges. Firstly, the augmentation step might not be able to generate positive and negative samples that are semantically similar and dissimilar to the anchor respectively. Secondly, the text data could be enhanced with external auxiliary information that might introduce noise to the sparse text data. In addition, they are limited in capturing higher-order information such as group-wise interactions. In this work, we propose a novel document simplicial complex construction based on text data for a higher-order message-passing mechanism. We enhance the short text classification performance by contrasting the structural representation with the sequential representation generated by the transformer mechanism for improved outcomes and mitigated issues. The proposed framework, Contrastive Learning with Simplicial Convolutional Networks (C-SCN), leverages the expressive power of graph neural networks, models higher-order information beyond pair-wise relations and enriches features through contrastive learning. Experimental results on four benchmark datasets demonstrate the capability of C-SCN to outperform existing models in analysing sequential and complex short-text data.
|
Topological Deep Learning, Representation Learning, Message-Passing Mechanism
| null | 14,613 | null |
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] | null |
333 |
Zero-Inflated Bandits
|
https://openreview.net/forum?id=DRvtabzN0n
|
[
"Haoyu Wei",
"Runzhe Wan",
"Lei Shi",
"Rui Song"
] |
Poster
|
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful distribution modeling is recognized as essential for improving estimation efficiency in statistics. However, this approach remains under-explored in the context of bandits. To address this gap, we initiate the study of zero-inflated bandits, where the reward is modeled using a classic semi-parametric distribution known as the zero-inflated distribution. We develop algorithms based on the Upper Confidence Bound and Thompson Sampling frameworks for this specific structure. The superior empirical performance of these methods is demonstrated through extensive numerical studies.
|
Bandits, Zero-Inflated, Exploration
| null | 14,608 |
2312.15595
|
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] | null |
334 |
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
|
https://openreview.net/forum?id=z83rodY0Pw
|
[
"Samir Khaki",
"Xiuyu Li",
"Junxian Guo",
"Ligeng Zhu",
"Konstantinos N. Plataniotis",
"Amir Yazdanbakhsh",
"Kurt Keutzer",
"Song Han",
"Zhijian Liu"
] |
Poster
|
Fine-tuning LLMs is both computationally and
memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA,
reduce the number of trainable parameters and
lower memory usage, they do not decrease computational cost. In some cases, they may even
slow down fine-tuning. In this paper, we introduce
SparseLoRA, a method that accelerates LLM fine-tuning through contextual sparsity. We propose
a lightweight, training-free SVD sparsity estimator that dynamically selects a sparse subset of
weights for loss and gradient computation. Also,
we systematically analyze and address sensitivity
across layers, tokens, and training steps. Our experimental results show that SparseLoRA reduces
computational cost by up to $2.0\times$ and a measured
speedup of up to $1.5\times$ while maintaining accuracy across various downstream tasks, including
commonsense and arithmetic reasoning, code generation, and instruction following.
|
large language models, efficient fine-tuning, sparsity, peft
|
This paper introduces SparseLoRA, a method that uses contextual sparsity to accelerate LLM fine-tuning, cutting compute by up to 2× and runtime by 1.5× while maintaining model accuracy on reasoning, coding, and instruction-following tasks.
| 14,607 |
2506.16500
|
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] |
https://github.com/z-lab/sparselora
|
335 |
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces
|
https://openreview.net/forum?id=AjbiIcRt6q
|
[
"Kevin Rojas",
"Yuchen Zhu",
"Sichen Zhu",
"Felix X-F. Ye",
"Molei Tao"
] |
Poster
|
Diffusion models have demonstrated remarkable performance in generating unimodal data across various tasks, including image, video, and text generation. On the contrary, the joint generation of multimodal data through diffusion models is still in the early stages of exploration. Existing approaches heavily rely on external preprocessing protocols, such as tokenizers and variational autoencoders, to harmonize varied data representations into a unified, unimodal format. This process heavily demands the high accuracy of encoders and decoders, which can be problematic for applications with limited data. To lift this restriction, we propose a novel framework for building multimodal diffusion models on arbitrary state spaces, enabling native generation of coupled data across different modalities. By introducing an innovative decoupled noise schedule for each modality, we enable both unconditional and modality-conditioned generation within a single model simultaneously. We empirically validate our approach for text-image generation and mixed-type tabular data synthesis, demonstrating that it achieves competitive performance.
|
Diffusion Model, Discrete Diffusion, Multimodal, Generative Modeling
| null | 14,606 |
2506.07903
|
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] |
https://github.com/KevinRojas1499/Diffuse-Everything
|
336 |
Accelerating Large Language Model Reasoning via Speculative Search
|
https://openreview.net/forum?id=oq0t5BXilT
|
[
"Zhihai Wang",
"Jie Wang",
"Jilai Pan",
"Xilin Xia",
"Huiling Zhen",
"Mingxuan Yuan",
"Jianye HAO",
"Feng Wu"
] |
Poster
|
Tree-search-based reasoning methods have significantly enhanced the reasoning capability of large language models (LLMs) by facilitating the exploration of multiple intermediate reasoning steps, i.e., thoughts. However, these methods suffer from substantial inference latency, as they have to generate numerous reasoning thoughts, severely limiting LLM applicability. To address this challenge, we propose a novel Speculative Search (SpecSearch) framework that significantly accelerates LLM reasoning by optimizing thought generation. Specifically, SpecSearch utilizes a small model to strategically collaborate with a large model at both thought and token levels, efficiently generating high-quality reasoning thoughts. The major pillar of SpecSearch is a novel quality-preserving rejection mechanism, which effectively filters out thoughts whose quality falls below that of the large model's outputs. Moreover, we show that SpecSearch preserves comparable reasoning quality to the large model. Experiments on both the Qwen and Llama models demonstrate that SpecSearch significantly outperforms state-of-the-art approaches, achieving up to 2.12$\times$ speedup with comparable reasoning quality.
|
Large Language Model Reasoning, Inference Acceleration, Tree Search, Speculative Execution
| null | 14,604 |
2505.02865
|
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] |
https://github.com/MIRALab-USTC/LLMReasoning-SpecSearch
|
337 |
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
|
https://openreview.net/forum?id=L1Bm396P0X
|
[
"Gül Sena Altıntaş",
"Devin Kwok",
"Colin Raffel",
"David Rolnick"
] |
Poster
|
Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is unclear to what extent such effects lead to meaningfully different networks, either in terms of the models' weights or the underlying functions that were learned. In this work, we show that during the initial "chaotic" phase of training, even extremely small perturbations reliably causes otherwise identical training trajectories to diverge-an effect that diminishes rapidly over training time. We quantify this divergence through (i) $L^2$ distance between parameters, (ii) the loss barrier when interpolating between networks, (iii) $L^2$ and barrier between parameters after permutation alignment, and (iv) representational similarity between intermediate activations; revealing how perturbations across different hyperparameter or fine-tuning settings drive training trajectories toward distinct loss minima. Our findings provide insights into neural network training stability, with practical implications for fine-tuning, model merging, and diversity of model ensembles.
|
early training, linear mode connectivity, loss landscape, perturbation sensitivity, permutation symmetry
|
Tiny perturbations cause neural network training to diverge to distinct loss basins
| 14,603 |
2506.13234
|
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] |
https://github.com/gsaltintas/lmc
|
338 |
OmiAD: One-Step Adaptive Masked Diffusion Model for Multi-class Anomaly Detection via Adversarial Distillation
|
https://openreview.net/forum?id=859NdHQv0Z
|
[
"Yaoxuan Feng",
"Wenchao Chen",
"Yuxin Li",
"Bo Chen",
"Yubiao Wang",
"Zixuan Zhao",
"Hongwei Liu",
"Mingyuan Zhou"
] |
Poster
|
Diffusion models have demonstrated outstanding performance in industrial anomaly detection. However, their iterative denoising nature results in slow inference speed, limiting their practicality for real-time industrial deployment. To address this challenge, we propose OmiAD, a one-step masked diffusion model for multi-class anomaly detection, derived from a well-designed multi-step **A**daptive **M**asked **D**iffusion **M**odel (AMDM) and compressed using **A**dversarial **S**core **D**istillation (ASD). OmiAD first introduces AMDM, equipped with an adaptive masking strategy that dynamically adjusts masking patterns based on noise levels and encourages the model to reconstruct anomalies as normal counterparts by leveraging broader context, to reduce the pixel-level shortcut reliance. Then, ASD is developed to compress the multi-step diffusion process into a single-step generator by score distillation and incorporating a shared-weight discriminator effectively reusing parameters while significantly improving both inference efficiency and detection performance. The effectiveness of OmiAD is validated on four diverse datasets, achieving state-of-the-art performance across seven metrics while delivering a remarkable inference speedup.
|
Anomaly Detection, Diffusion Model, Diffusion Distillation
| null | 14,587 | null |
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] | null |
339 |
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
|
https://openreview.net/forum?id=MOlihFnYNU
|
[
"Zihan Chen",
"Song Wang",
"Zhen Tan",
"Jundong Li",
"Cong Shen"
] |
Poster
|
In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples, known as demonstrations, into the input of LLMs. More recently, advancements in the expanded context windows of LLMs have led to many-shot ICL, which uses hundreds of demonstrations and outperforms few-shot ICL, which relies on fewer examples. However, this approach is often hindered by the high cost of obtaining large amounts of labeled data. To address this challenge, we propose **M**any-Shot **A**daptive **P**seudo-**L**ab**E**ling, namely **MAPLE**, a novel influence-based many-shot ICL framework that utilizes pseudo-labeled samples to compensate for the lack of label information.
We first identify a subset of impactful unlabeled samples and perform pseudo-labeling on them by querying LLMs. These pseudo-labeled samples are then adaptively selected and tailored to each test query as input to improve the performance of many-shot ICL, without significant labeling costs.
Extensive experiments on real-world datasets demonstrate the effectiveness of our framework, showcasing its ability to enhance LLM adaptability and performance with limited labeled data. Our code is provided at https://github.com/Chen-1031/MAPLE_ICL.
|
In-context learning, Many-shot In-context learning, Large language models
| null | 14,586 |
2505.16225
|
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] |
https://github.com/chen-1031/maple_icl
|
340 |
The Limits of Tractable Marginalization
|
https://openreview.net/forum?id=cTsYaXSm9O
|
[
"Oliver Broadrick",
"Sanyam Agarwal",
"Guy Van den Broeck",
"Markus Bläser"
] |
Poster
|
Marginalization -- summing a function over all assignments to a subset of its inputs -- is a fundamental computational problem with applications from probabilistic inference to formal verification.
Despite its computational hardness in general, there exist many classes of functions (e.g., probabilistic models) for which marginalization remains tractable, and they can all be commonly expressed by arithmetic circuits computing multilinear polynomials.
This raises the question, can *all* functions with polynomial time marginalization algorithms be succinctly expressed by such circuits? We give a negative answer, exhibiting simple functions with tractable marginalization yet no efficient representation by known models, assuming $\\mathsf{FP} \\neq \\#\\mathsf{P}$ (an assumption implied by $\\mathsf{P} \\neq \\mathsf{NP}$).
To this end, we identify a hierarchy of complexity classes corresponding to stronger forms of marginalization, all of which are efficiently computable on the known circuit models.
We conclude with a completeness result, showing that whenever there is an efficient real RAM performing virtual evidence marginalization for a function, then there are small arithmetic circuits for that function's multilinear representation.
|
probabilistic inference, tractable models, expressive-efficiency
|
We show that the many known models for tractable marginalization have not yet captured all functions with tractable marginalization.
| 14,577 |
2506.12020
|
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] | null |
341 |
e-GAI: e-value-based Generalized α-Investing for Online False Discovery Rate Control
|
https://openreview.net/forum?id=Pedm1880A2
|
[
"Yifan Zhang",
"Zijian Wei",
"Haojie Ren",
"Changliang Zou"
] |
Poster
|
Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $\alpha$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from a significant loss in power as testing levels are derived from pre-specified descent sequences. To address these limitations, we propose a novel framework on valid e-values named e-GAI. The proposed e-GAI can ensure provable online FDR control under more general dependency conditions while improving the power by dynamically allocating the testing levels. These testing levels are updated not only by relying on both the number of previous rejections and the prior costs, but also, differing from the GAI framework, by assigning less $\alpha$-wealth for each rejection from a risk aversion perspective. Within the e-GAI framework, we introduce two new online FDR procedures, e-LORD and e-SAFFRON, and provide strategies for the long-term performance to address the issue of $\alpha$-death, a common phenomenon within the GAI framework. Furthermore, we demonstrate that e-GAI can be generalized to conditionally super-uniform p-values. Both simulated and real data experiments demonstrate the advantages of both e-LORD and e-SAFFRON in FDR control and power.
|
online multiple testing, generalized $\alpha$-investing, e-value, false discovery rate
| null | 14,560 | null |
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] |
https://github.com/zijianwei01/e-GAI
|
342 |
NETS: A Non-equilibrium Transport Sampler
|
https://openreview.net/forum?id=QqGw9StPbQ
|
[
"Michael Samuel Albergo",
"Eric Vanden-Eijnden"
] |
Poster
|
We introduce the Non-Equilibrium Transport Sampler (NETS), an algorithm for sampling from unnormalized probability distributions. NETS builds on non-equilibrium sampling strategies that transport a simple base distribution into the target distribution in finite time, as pioneered in Neal's annealed importance sampling (AIS). In the continuous-time setting, this transport is accomplished by evolving walkers using Langevin dynamics with a time-dependent potential, while simultaneously evolving importance weights to debias their solutions following Jarzynski's equality. The key innovation of NETS is to add to the dynamics a learned drift term that offsets the need for these corrective weights by minimizing their variance through an objective that can be estimated without backpropagation and provably bounds the Kullback-Leibler divergence between the estimated and target distributions. NETS provides unbiased samples and features a tunable diffusion coefficient that can be adjusted after training to maximize the effective sample size. In experiments on standard benchmarks, high-dimensional Gaussian mixtures, and statistical lattice field theory models, NETS shows compelling performances.
|
jarzynski, sampling, generative models, diffusion, monte carlo
|
An augmentation of sequential monte carlo and annealed importance sampling with transport, unbiased with IS weights, with connections to non-equilibrium physics.
| 14,559 |
2410.02711
|
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] | null |
343 |
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
|
https://openreview.net/forum?id=hmGhP5DO2W
|
[
"Marwa Abdulhai",
"Isadora White",
"Charlie Victor Snell",
"Charles Sun",
"Joey Hong",
"Yuexiang Zhai",
"Kelvin Xu",
"Sergey Levine"
] |
Poster
|
Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed agents and might necessitate considerable prompt tuning. Even the best current LLMs rarely ask clarifying questions, engage in explicit information gathering, or take actions that lead to better decisions after multiple turns. Reinforcement learning has the potential to leverage the powerful modeling capabilities of LLMs, as well as their internal representation of textual interactions, to create capable goal-directed language agents. This can enable intentional and temporally extended interactions, such as with humans, the emergence of complex skills such as persuasion, and long-horizon strategic behavior, such as in the context of games. Enabling this requires the community to develop reliable reinforcement learning algorithms for training LLMs. Developing such algorithms requires tasks that can gauge progress on algorithm design, provide accessible and reproducible evaluations for multi-turn interactions, and cover a range of task properties and challenges in improving reinforcement learning algorithms. Our paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for LLMs, together with an open-source research framework for getting started on multi-turn RL with offline value-based and online policy-based RL methods. Our benchmark consists of 3 Interactive Dialogue tasks and 5 RL Capability tests for a total of 8 tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
|
a benchmark for enabling development of RL algorithms for language tasks
|
a benchmark for enabling development of RL algorithms for language tasks
| 14,551 |
2311.18232
|
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] |
https://github.com/abdulhaim/LMRL-Gym
|
344 |
Open Materials Generation with Stochastic Interpolants
|
https://openreview.net/forum?id=gHGrzxFujU
|
[
"Philipp Höllmer",
"Thomas Egg",
"Maya Martirossyan",
"Eric Fuemmeler",
"Zeren Shui",
"Amit Gupta",
"Pawan Prakash",
"Adrian Roitberg",
"Mingjie Liu",
"George Karypis",
"Mark Transtrum",
"Richard Hennig",
"Ellad B. Tadmor",
"Stefano Martiniani"
] |
Poster
|
The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of stable crystal structures within an infinite design space. We introduce Open Materials Generation (OMatG), a unifying framework for the generative design and discovery of inorganic crystalline materials. OMatG employs stochastic interpolants (SI) to bridge an arbitrary base distribution to the target distribution of inorganic crystals via a broad class of tunable stochastic processes, encompassing both diffusion models and flow matching as special cases. In this work, we adapt the SI framework by integrating an equivariant graph representation of crystal structures and extending it to account for periodic boundary conditions in unit cell representations. Additionally, we couple the SI flow over spatial coordinates and lattice vectors with discrete flow matching for atomic species. We benchmark OMatG's performance on two tasks: Crystal Structure Prediction (CSP) for specified compositions, and de novo generation (DNG) aimed at discovering stable, novel, and unique structures. In our ground-up implementation of OMatG, we refine and extend both CSP and DNG metrics compared to previous works. OMatG establishes a new state of the art in generative modeling for materials discovery, outperforming purely flow-based and diffusion-based implementations. These results underscore the importance of designing flexible deep learning frameworks to accelerate progress in materials science. The OMatG code is available at https://github.com/FERMat-ML/OMatG.
|
stochastic interpolants, generative models, inorganic crystals, materials design
| null | 14,547 |
2502.02582
|
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] |
https://github.com/FERMat-ML/OMatG
|
345 |
Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
|
https://openreview.net/forum?id=hk7CBybb6x
|
[
"Michal Lukasik",
"Lin Chen",
"Harikrishna Narasimhan",
"Aditya Krishna Menon",
"Wittawat Jitkrittum",
"Felix X. Yu",
"Sashank J. Reddi",
"Gang Fu",
"Mohammadhossein Bateni",
"Sanjiv Kumar"
] |
Poster
|
Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under the ROC curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How can one synthesize such labels into a single coherent ranking? In this work, we formally analyze two approaches to this problem—loss aggregation and label aggregation—by characterizing their Bayes-optimal solutions. We show that while both approaches can yield Pareto-optimal solutions, loss aggregation can exhibit label dictatorship: one can inadvertently (and undesirably) favor one label over others. This suggests that label aggregation can be preferable to loss aggregation, which we empirically verify.
|
learning to rank, area under the curve, multi-objective optimization
| null | 14,546 |
2504.11284
|
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] | null |
346 |
KernelBench: Can LLMs Write Efficient GPU Kernels?
|
https://openreview.net/forum?id=yeoN1iQT1x
|
[
"Anne Ouyang",
"Simon Guo",
"Simran Arora",
"Alex L Zhang",
"William Hu",
"Christopher Re",
"Azalia Mirhoseini"
] |
Poster
|
Efficient GPU kernels are crucial for building performant machine learning architectures, but writing them is a time-consuming challenge that requires significant expertise; therefore, we explore using language models (LMs) to automate kernel generation. We introduce **KernelBench**, an open-source framework for evaluating LMs' ability to write fast and correct kernels on a suite of 250 carefully selected PyTorch ML workloads. KernelBench represents a real-world engineering environment and making progress on the introduced benchmark directly translates to faster practical kernels. We introduce a new evaluation metric $\text{fast}_p$, which measures the percentage of generated kernels that are functionally correct and offer a speedup greater than an adjustable threshold $p$ over baseline. Our experiments across various state-of-the-art models and test-time methods show that frontier reasoning models perform the best out of the box but still fall short overall, matching the PyTorch baseline in less than 20\% of the cases. While we show that results can improve by leveraging execution and profiling feedback during iterative refinement, KernelBench remains a challenging benchmark, with its difficulty increasing as we raise speedup threshold $p$.
|
Benchmark, GPU Kernel Design, Code Generation
|
A benchmark evaluating language models’ ability to generate correct and fast GPU kernels
| 14,542 |
2502.10517
|
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] |
https://github.com/ScalingIntelligence/KernelBench
|
347 |
LLM-Augmented Chemical Synthesis and Design Decision Programs
|
https://openreview.net/forum?id=NhkNX8jYld
|
[
"Haorui Wang",
"Jeff Guo",
"Lingkai Kong",
"Rampi Ramprasad",
"Philippe Schwaller",
"Yuanqi Du",
"Chao Zhang"
] |
Poster
|
Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design.
|
Large Language models, Retrosynthesis planning, Molecule design
| null | 14,541 |
2505.07027
|
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] | null |
348 |
Efficient Bisection Projection to Ensure Neural-Network Solution Feasibility for Optimization over General Set
|
https://openreview.net/forum?id=HWN9CAfcav
|
[
"Enming Liang",
"Minghua Chen"
] |
Poster
|
Neural networks (NNs) have emerged as promising tools for solving constrained optimization problems in real-time. However, ensuring constraint satisfaction for NN-generated solutions remains challenging due to prediction errors. Existing methods to ensure NN feasibility either suffer from high computational complexity or are limited to specific constraint types.
We present Bisection Projection, an efficient approach to ensure NN solution feasibility for optimization over general compact sets with non-empty interiors.
Our method comprises two key components:
(i) a dedicated NN (called IPNN) that predicts interior points (IPs) with low eccentricity, which naturally accounts for approximation errors;
(ii) a bisection algorithm that leverages these IPs to recover solution feasibility when initial NN solutions violate constraints.
We establish theoretical guarantees by providing sufficient conditions for IPNN feasibility and proving bounded optimality loss of the bisection operation under IP predictions.
Extensive evaluations on real-world non-convex problems demonstrate that Bisection Projection achieves superior feasibility and computational efficiency compared to existing methods, while maintaining comparable optimality gaps.
|
Neural Network, Constraint, Feasibility, Projection, Interior Point
|
An efficient bisection-based projection scheme to ensure NN solution feasibility over general compact sets.
| 14,540 | null |
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] | null |
349 |
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
|
https://openreview.net/forum?id=LWH8yn4HS2
|
[
"Bernal Jiménez Gutiérrez",
"Yiheng Shu",
"Weijian Qi",
"Sizhe Zhou",
"Yu Su"
] |
Poster
|
Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential. Given the challenges in continual learning with large language models (LLMs), retrieval-augmented generation (RAG) has become the dominant way to introduce new information. However, its reliance on vector retrieval hinders its ability to mimic the dynamic and interconnected nature of human long-term memory. Recent RAG approaches augment vector
embeddings with various structures like knowledge graphs to address some of these gaps, namely sense-making and associativity. However, their performance on more basic factual memory tasks drops considerably below standard RAG. We address this unintended deterioration and propose HippoRAG 2, a framework that outperforms standard RAG comprehensively on factual, sense-making, and associative memory tasks. HippoRAG 2 builds upon the Personalized PageRank algorithm used in HippoRAG and enhances it with deeper passage integration and more effective online use of an LLM. This combination pushes this RAG system closer to the effectiveness of human long-term memory, achieving a 7% improvement in associative memory tasks over the state-of-the-art embedding model while also exhibiting superior factual knowledge and sense-making memory capabilities. This work paves the way for non-parametric continual learning for LLMs. Code and data are available at https://github.com/OSU-NLP-Group/HippoRAG.
|
retrieval-augmented generation, large language models, long-term memory, continual learning
|
In this work, we propose HippoRAG 2, a non-parametric continual learning framework which outperforms state-of-the-art retrieval methods on factual, sense-making, and associative memory tasks.
| 14,527 |
2502.14802
|
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] |
https://github.com/OSU-NLP-Group/HippoRAG
|
350 |
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
|
https://openreview.net/forum?id=zy15E0X3Dq
|
[
"Mohit Pandey",
"Gopeshh Subbaraj",
"Artem Cherkasov",
"Martin Ester",
"Emmanuel Bengio"
] |
Poster
|
Generative Flow Networks (GFlowNets) have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from rewards treated as unnormalized distributions. Previous works in this framework often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using drug-like molecule datasets, which teaches A-GFNs about inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further implement a goal-conditioned finetuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on a subset of ZINC dataset, and by employing robust evaluation metrics we show the effectiveness of our approach when compared to other relevant baseline methods for a wide range of drug design tasks. The code is accessible at https://github.com/diamondspark/AGFN.
|
Generative Flow Networks, Foundation model, Drug Discovery
|
We introduce Atomic GFlowNets, a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively.
| 14,523 |
2503.06337
|
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] |
https://github.com/diamondspark/AGFN
|
351 |
Training High Performance Spiking Neural Network by Temporal Model Calibration
|
https://openreview.net/forum?id=l7ZmdeFyM1
|
[
"Jiaqi Yan",
"Changping Wang",
"De Ma",
"Huajin Tang",
"Qian Zheng",
"Gang Pan"
] |
Poster
|
Spiking Neural Networks (SNNs) are considered promising energy-efficient models due to their dynamic capability to process spatial-temporal spike information. Existing work has demonstrated that SNNs exhibit temporal heterogeneity, which leads to diverse outputs of SNNs at different time steps and has the potential to enhance their performance. Although SNNs obtained by direct training methods achieve state-of-the-art performance, current methods introduce limited temporal heterogeneity through the dynamics of spiking neurons or network structures. They lack the improvement of temporal heterogeneity through the lens of the gradient. In this paper, we first conclude that the diversity of the temporal logit gradients in current methods is limited. This leads to insufficient temporal heterogeneity and results in temporally miscalibrated SNNs with degraded performance. Based on the above analysis, we propose a Temporal Model Calibration (TMC) method, which can be seen as a logit gradient rescaling mechanism across time steps. Experimental results show that our method can improve the temporal logit gradient diversity and generate temporally calibrated SNNs with enhanced performance. In particular, our method achieves state-of-the-art accuracy on ImageNet, DVSCIFAR10, and N-Caltech101. Codes are available at https://github.com/zju-bmi-lab/TMC.
|
Spiking Neural Networks, Direct Training, Temporal Heterogeneity, Temporal Model Calibration
| null | 14,510 | null |
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] | null |
352 |
Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning
|
https://openreview.net/forum?id=0E5rZOGA13
|
[
"Hao Wang",
"Zhichao Chen",
"Haotian Wang",
"Yanchao Tan",
"Licheng Pan",
"Tianqiao Liu",
"Xu Chen",
"Haoxuan Li",
"Zhouchen Lin"
] |
Poster
|
Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples. This assumption risks misclassifying potential positive samples within the unlabeled data, thereby undermining model performance. To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior. To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples. Experiments on three real-world datasets validate the efficacy of PURL in terms of improved recommendation quality. Code is available at https://github.com/HowardZJU/weakrec.
|
recommendation, collaborative filtering
| null | 14,506 | null |
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] | null |
353 |
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
|
https://openreview.net/forum?id=3zWSmhNSa7
|
[
"Jiachang Liu",
"Soroosh Shafiee",
"Andrea Lodi"
] |
Poster
|
This paper investigates the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through an $\ell_0$ cardinality constraint. While branch-and-bound (BnB) frameworks can certify optimality by pruning nodes using dual bounds, existing methods for computing these bounds are either computationally intensive or exhibit slow convergence, limiting their scalability to large-scale problems. To address this challenge, we propose a first-order proximal gradient algorithm designed to solve the perspective relaxation of the problem within a BnB framework. Specifically, we formulate the relaxed problem as a composite optimization problem and demonstrate that the proximal operator of the non-smooth component can be computed exactly in log-linear time complexity, eliminating the need to solve a computationally expensive second-order cone program. Furthermore, we introduce a simple restart strategy that enhances convergence speed while maintaining low per-iteration complexity. Extensive experiments on synthetic and real-world datasets show that our approach significantly accelerates dual bound computations and is highly effective in providing optimality certificates for large-scale problems.
|
generalized linear models, sparse learning, proximal method, mixed-integer programming
| null | 14,502 | null |
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] | null |
354 |
DataDecide: How to Predict Best Pretraining Data with Small Experiments
|
https://openreview.net/forum?id=p9YlQPF8fE
|
[
"Ian Magnusson",
"Nguyen Tai",
"Ben Bogin",
"David Heineman",
"Jena D. Hwang",
"Luca Soldaini",
"Akshita Bhagia",
"Jiacheng Liu",
"Dirk Groeneveld",
"Oyvind Tafjord",
"Noah A. Smith",
"Pang Wei Koh",
"Jesse Dodge"
] |
Poster
|
Because large language models are expensive to pretrain on different datasets, using smaller-scale experiments to decide on data is crucial for reducing costs. Which benchmarks and methods of making decisions from observed performance at small scale most accurately predict the datasets that yield the best large models? To empower open exploration of this question, we release models, data, and evaluations in DataDecide—the most extensive open suite of models over differences in data and scale. We conduct controlled pretraining experiments across 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, model sizes up to 1B parameters, and 3 random seeds. We find that the ranking of models at a single, small size (e.g., 150M parameters) is a strong baseline for predicting best models at our larger target scale (1B) (∼ 80% of comparisons correct). No scaling law methods among 8 baselines exceed the compute-decision frontier of single-scale predictions, but DataDecide can measure improvement in future scaling laws. We also identify that using continuous likelihood metrics as proxies in small experiments makes benchmarks including MMLU, ARC, HellaSwag, MBPP, and HumanEval > 80% predictable at the target 1B scale with just 0.01% of the compute.
|
Pretraining Data, Language Models, Scaling Laws, Evaluation, Benchmarks, Data Recipes, Data Ablations
|
When do pretraining experiments at small scales correctly predict the ranking of different data recipes at larger scales as measured by downstream performance?
| 14,500 |
2504.11393
|
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] |
https://github.com/allenai/DataDecide
|
355 |
On the Query Complexity of Verifier-Assisted Language Generation
|
https://openreview.net/forum?id=9oIjvaDhoN
|
[
"Edoardo Botta",
"Yuchen Li",
"Aashay Mehta",
"Jordan T. Ash",
"Cyril Zhang",
"Andrej Risteski"
] |
Poster
|
Recently, a plethora of works have proposed
inference-time algorithms (e.g. best-of-n), which
incorporate verifiers to assist the generation process. Their quality-efficiency trade-offs have been
empirically benchmarked on a variety of constrained generation tasks, but the algorithmic design landscape is still largely poorly understood.
In this paper, we develop a mathematical framework for reasoning about constrained generation
using a pre-trained language model generator oracle and a process verifier—which can decide
whether a prefix can be extended to a string which
satisfies the constraints of choice. We show that
even in very simple settings, access to a verifier
can render an intractable problem (information-theoretically or computationally) to a tractable
one. In fact, we show even simple algorithms,
like tokenwise rejection sampling, can enjoy significant benefits from access to a verifier. Empirically, we show that a natural modification of
tokenwise rejection sampling, in which the sampler is allowed to "backtrack" (i.e., erase the final
few generated tokens) has robust and substantive
benefits over natural baselines (e.g. (blockwise)
rejection sampling, nucleus sampling)—both in
terms of computational efficiency, accuracy and
diversity.
|
verifier-assisted language generation, query complexity, inference-time scaling, best-of-N sampling, constrained generation, theory
| null | 14,496 |
2502.12123
|
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] |
https://github.com/YuchenLi01/LM_Query_Complexity
|
356 |
Dimension-Free Adaptive Subgradient Methods with Frequent Directions
|
https://openreview.net/forum?id=zpuKPuSYru
|
[
"Sifan Yang",
"Yuanyu Wan",
"Peijia Li",
"Yibo Wang",
"Xiao Zhang",
"Zhewei Wei",
"Lijun Zhang"
] |
Poster
|
In this paper, we investigate the acceleration of adaptive subgradient methods through frequent directions (FD), a widely-used matrix sketching technique. The state-of-the-art regret bound exhibits a _linear_ dependence on the dimensionality $d$, leading to unsatisfactory guarantees for high-dimensional problems. Additionally, it suffers from an $O(\tau^2 d)$ time complexity per round, which scales quadratically with the sketching size $\tau$. To overcome these issues, we first propose an algorithm named FTSL, achieving a tighter regret bound that is independent of the dimensionality. The key idea is to integrate FD with adaptive subgradient methods under _the primal-dual framework_ and add the cumulative discarded information of FD back. To reduce its time complexity, we further utilize fast FD to expedite FTSL, yielding a better complexity of $O(\tau d)$ while maintaining the same regret bound. Moreover, to mitigate the computational cost for optimization problems involving matrix variables (e.g., training neural networks), we adapt FD to Shampoo, a popular optimization algorithm that accounts for the structure of decision, and give a novel analysis under _the primal-dual framework_. Our proposed method obtains an improved dimension-free regret bound. Experimental results have verified the efficiency and effectiveness of our approaches.
|
Online Convex Optimization, Frequent Directions, Adaptive Method
| null | 14,491 | null |
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] | null |
357 |
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
|
https://openreview.net/forum?id=Xg1BGlybfq
|
[
"Yitian Zhang",
"Liheng Ma",
"Antonios Valkanas",
"Boris N. Oreshkin",
"Mark Coates"
] |
Poster
|
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a space of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dimensional. Therefore, the objective is to learn measurement functions that yield a tractable finite-dimensional Koopman operator approximation. In this work, we establish a connection between Koopman operator approximation and linear Recurrent Neural Networks (RNNs), which have recently demonstrated remarkable success in sequence modeling. We show that by considering an extended state consisting of lagged observations, we can establish an equivalence between a structured Koopman operator and linear RNN updates. Building on this connection, we present SKOLR, which integrates a learnable spectral decomposition of the input signal with a multilayer perceptron (MLP) as the measurement functions and implements a structured Koopman operator via a highly parallel linear RNN stack. Numerical experiments on various forecasting benchmarks and dynamical systems show that this streamlined, Koopman-theory-based design delivers exceptional performance. Our code is available at: https://github.com/networkslab/SKOLR.
|
Koopman Operator Theory, Linear RNN, Time-Series Forecasting
| null | 14,482 |
2506.14113
|
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] | null |
358 |
Stochastic Deep Restoration Priors for Imaging Inverse Problems
|
https://openreview.net/forum?id=Km3QvYPmK4
|
[
"Yuyang Hu",
"Albert Peng",
"Weijie Gan",
"Peyman Milanfar",
"Mauricio Delbracio",
"Ulugbek S. Kamilov"
] |
Poster
|
Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. We introduce Stochastic deep Restoration Priors (ShaRP), a novel framework that stochastically leverages an ensemble of deep restoration models beyond denoisers to regularize inverse problems. By using generalized restoration models trained on a broad range of degradations beyond simple Gaussian noise, ShaRP effectively addresses structured artifacts and enables self-supervised training without fully sampled data. We prove that ShaRP minimizes an objective function involving a regularizer derived from the score functions of minimum mean square error (MMSE) restoration operators. We also provide theoretical guarantees for learning restoration operators from incomplete measurements. ShaRP achieves state-of-the-art performance on tasks such as magnetic resonance imaging reconstruction and single-image super-resolution, surpassing both denoiser- and diffusion-model-based methods without requiring retraining.
|
Inverse Problems, Image Reconstruction, Restoration Priors
|
We introduce ShaRP, a novel framework that stochastically leverages an ensemble of deep restoration models beyond denoisers to regularize inverse problems.
| 14,452 |
2410.02057
|
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] | null |
359 |
Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
|
https://openreview.net/forum?id=3YTjTQhX8B
|
[
"Su Jia",
"Peter I. Frazier",
"Nathan Kallus"
] |
Poster
|
Experimentation with interference poses a significant challenge in contemporary online platforms. Prior research on experimentation with interference has concentrated on the final output of a policy. Cumulative performance, while equally important, is less well understood. To address this gap, we introduce the problem of Multi-armed Bandits with Interference (MABI), where the learner assigns an arm to each of $N$ experimental units over $T$ rounds. The reward of each unit in each round depends on the treatments of all units, where the interference between two units decays in their distance. The reward functions are chosen by an adversary and may vary arbitrarily over time and across different units. We first show that the optimal expected regret (against the best fixed-arm policy) is $\tilde O(\sqrt T)$, and can be achieved by a switchback policy. However, the regret (as a random variable) for any switchback policy suffers a high variance, since it does not account for $N$. We propose a policy based on a novel clustered randomization scheme, whose regret (i) is optimal in expectation and (ii) admits a high probability bound that vanishes in $N$.
|
interference, adversarial bandits, high-probability regret bound
|
We study a multiple-play adversarial bandits problem where the reward of a unit depends on the arms of ALL units
| 14,449 | null |
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] | null |
360 |
Leveraging Per-Instance Privacy for Machine Unlearning
|
https://openreview.net/forum?id=0A4Y9qRnu9
|
[
"Nazanin Mohammadi Sepahvand",
"Anvith Thudi",
"Berivan Isik",
"Ashmita Bhattacharyya",
"Nicolas Papernot",
"Eleni Triantafillou",
"Daniel M. Roy",
"Gintare Karolina Dziugaite"
] |
Poster
|
We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility–unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ´enyi) divergence to retraining without an individual datapoint. To demonstrate the practical applicability of our theory, we present empirical results showing that our theoretical predictions are born out both for Stochastic Gradient Langevin Dynamics (SGLD) as well as for standard fine-tuning without explicit noise. We further demonstrate that per-instance privacy losses correlate well with several existing data difficulty metrics, while also
identifying harder groups of data points, and introduce novel evaluation methods based on loss barriers. All together, our findings provide a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points.
|
machine unlearning, differential privacy
|
We show that per-instance privacy levels, computed during training, provide a practical and reliable way to predict unlearning difficulty in fine-tuning-based methods, enabling more efficient and targeted unlearning strategies.
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2505.18786
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] | null |
361 |
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
|
https://openreview.net/forum?id=EMHED4WTHT
|
[
"Liang CHEN",
"Xueting Han",
"Li Shen",
"Jing Bai",
"Kam-Fai Wong"
] |
Poster
|
Harmful fine-tuning (HFT), performed directly on open-source LLMs or through Fine-tuning-as-a-Service, breaks safety alignment and poses significant threats. Existing methods aim to mitigate HFT risks by learning robust representation on alignment data or making harmful data unlearnable, but they treat each data sample equally, leaving data vulnerability patterns understudied. In this work, we reveal that certain subsets of alignment data are consistently more prone to forgetting during HFT across different fine-tuning tasks and exhibit lower robustness compared to other subsets. Inspired by these findings, we propose Vulnerability-Aware Alignment (VAA), which calculates data vulnerability, partitions data into "vulnerable" and "invulnerable" groups, and encourages balanced learning using a group distributionally robust optimization (Group DRO) framework. Specifically, VAA learns an adversarial sampler that samples examples from the currently underperforming group and then applies group-dependent adversarial perturbations to the data during training, aiming to encourage a balanced learning process across groups. Experiments across four fine-tuning tasks demonstrate that VAA significantly reduces harmful scores while preserving downstream task performance, outperforming state-of-the-art baselines.
|
large language models, robustness, safety alignment, harmful Fine-tuning, forgetting, group distributionally robust optimization, balance learning, ai safety
|
A new alignment method that improves safety in LLMs by identifying and upweighting vulnerable alignment data.
| 14,446 |
2506.03850
|
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] |
https://github.com/ChanLiang/VAA
|
362 |
Representation Preserving Multiclass Agnostic to Realizable Reduction
|
https://openreview.net/forum?id=yTfQt7vK6M
|
[
"Steve Hanneke",
"Qinglin Meng",
"Amirreza Shaeiri"
] |
Poster
|
We study the problem of multiclass classification when the number of labels can be unbounded within the PAC learning framework. Our main contribution is a theory that demonstrates a *simple* and *elegant* agnostic to realizable reduction for this framework. This resolves an open problem raised by the recent work of (Hopkins et al., 2022). Notably, our result is the first *representation preserving* multiclass agnostic to realizable reduction, in contrast with the compression based approach of the work of (David et al., 2017). Furthermore, our main theorem is stated in an abstract framework, called ``Unified PAC Learning'', which encompasses a range of frameworks, including multiclass PAC learning, list PAC learning, and multilabel PAC learning. In addition, we explore representation preserving reductions to the realizable setting for two noise models, namely Massart noise and Tsybakov noise, in the multiclass PAC learning framework. We believe our technique may find other applications in ensuing studies of theoretical machine learning.
|
PAC Learning, Agnostic to Realizable Reduction, Unified PAC Framework, Multiclass Learning, List Learning, Multilabel Learning, Noise Models, Partial Concept Classes
| null | 14,444 | null |
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] | null |
363 |
Optimal Fair Learning Robust to Adversarial Distribution Shift
|
https://openreview.net/forum?id=TGcXwWdQQj
|
[
"Sushant Agarwal",
"Amit Deshpande",
"Rajmohan Rajaraman",
"Ravi Sundaram"
] |
Poster
|
Previous work in fair machine learning has characterised the Fair Bayes Optimal Classifier (BOC) on a given distribution for both deterministic and randomized classifiers. We study the robustness of the Fair BOC to adversarial noise in the data distribution. Kearns & Li (1988) implies that the accuracy of the deterministic BOC without any fairness constraints is robust (Lipschitz) to malicious noise in the data distribution. We demonstrate that their robustness guarantee breaks down when we add fairness constraints. Hence, we consider the randomized Fair BOC, and our central result is that its accuracy is robust to malicious noise in the data distribution. Our robustness result applies to various fairness constraints---Demographic Parity, Equal Opportunity, Predictive Equality. Beyond robustness, we demonstrate that randomization leads to better accuracy and efficiency. We show that the randomized Fair BOC is nearly-deterministic, and gives randomized predictions on at most one data point, hence availing numerous benefits of randomness, while using very little of it.
|
Fairness, Randomization, Classification, Bayes-Optimal, Robustness, Distribution Shift, Adversarial Noise, Corrupted Data
| null | 14,431 | null |
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] | null |
364 |
Understanding Synthetic Context Extension via Retrieval Heads
|
https://openreview.net/forum?id=dNnA8ahuTY
|
[
"Xinyu Zhao",
"Fangcong Yin",
"Greg Durrett"
] |
Poster
|
Long-context LLMs are increasingly in demand for applications such as retrieval-augmented generation. To defray the cost of pretraining LLMs over long contexts, recent work takes an approach of synthetic context extension: fine-tuning LLMs with synthetically-generated long-context data. However, it remains unclear how and why this synthetic context extension imparts abilities for downstream long-context tasks. In this paper, we investigate fine-tuning on synthetic data for three long-context tasks that require retrieval and reasoning. We vary the realism of "needle'' concepts to be retrieved and diversity of the surrounding "haystack'' context, from using LLMs to construct synthetic documents to using templated relations and creating symbolic datasets. Although models trained on synthetic data underperform models trained on the real data, the impacts of both training settings can be understood via a shared feature of the attention computation, retrieval heads (Wu et al., 2024). The retrieval heads learned from synthetic data have high overlap with retrieval heads learned on real data. Furthermore, there is a strong correlation between the recall of heads learned and the downstream performance of a model, allowing us to interpret and predict the performance of models trained in different settings. Our results shed light on how to interpret synthetic data fine-tuning performance and how to approach creating better data for learning real-world LLM capabilities over long contexts.
|
Large Language Models, Synthetic Data, Long Context, Retrieval Heads
|
We identify when synthetic data falls short on learning long-context abilities and trace an explanation to a specific set of attention heads.
| 14,430 |
2410.22316
|
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] | null |
365 |
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)
|
https://openreview.net/forum?id=CAurIUGjkb
|
[
"Zihao Wang",
"Yibo Jiang",
"Jiahao Yu",
"Heqing Huang"
] |
Poster
|
Large language models (LLMs) that integrate multiple input roles (e.g., system instructions, user queries, external tool outputs) are increasingly prevalent in practice. Ensuring that the model accurately distinguishes messages from each role—a concept we call *role separation*—is crucial for consistent multi-role behavior. Although recent work often targets state-of-the-art prompt injection defenses, it remains unclear whether such methods truly teach LLMs to differentiate roles or merely memorize known triggers. In this paper, we examine *role-separation learning*: the process of teaching LLMs to robustly distinguish system and user tokens. Through a *simple, controlled experimental framework*, we find that fine-tuned models often rely on two proxies for role identification: (1) task type exploitation, and (2) proximity to begin-of-text. Although data augmentation can partially mitigate these shortcuts, it generally leads to iterative patching rather than a deeper fix. To address this, we propose reinforcing *invariant signals* that mark role boundaries by adjusting token-wise cues in the model's input encoding. In particular, modifying position IDs helps the model learn clearer distinctions and reduces reliance on superficial proxies. By focusing on this mechanism-centered perspective, our work illuminates how LLMs can more reliably maintain consistent multi-role behavior without merely memorizing known prompts or triggers.
|
LLM; role-separation; shortcut learning; position encoding
| null | 14,427 |
2505.00626
|
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] | null |
366 |
Safely Learning Optimal Auctions: A Testable Learning Framework for Mechanism Design
|
https://openreview.net/forum?id=ihRwpPYoKM
|
[
"Vikram Kher",
"Manolis Zampetakis"
] |
Poster
|
When can the distributional assumptions of theorems and learning algorithms be trusted? Inspired by this question, Rubinfeld and Vasilyan (2023) initiated the study of testable learning. In this schema, we always learn one of the following two things: either we have achieved the desired accuracy regardless of whether the distributional assumptions are satisfied, or the input distribution does not satisfy the original distributional assumptions. Motivated by the challenge of relying on strong distributional assumptions in many theorems in mechanism design, we develop a testable learning framework for mechanism design. Traditional models in mechanism design assume that value distributions satisfy some notion of regularity. Unfortunately, testing regularity is not possible in the original testable learning framework as we show. To bypass this impossibility, we propose a regularized version of the testable learning framework. Under this framework, we always learn one of the following two things: either we achieve high revenue compared to the best possible revenue of any regular distribution close to the input distribution, or the input distribution does not satisfy regularity. We then use this framework to provide: 1) a tester-learner pair for revenue optimal mechanisms, 2) a tester for whether the fundamental Bulow-Klemperer Theorem (Bulow and Klemperer 1996) is applicable to a given dataset, and 3) a tester to confirm the existence of an anonymous reserve price that results in the anonymous price auction securing a constant fraction of the optimal revenue.
|
Auction Theory, Mechanism Design, Testing Distributional Assumptions
|
We develop a testable learning framework for mechanism design and apply it to important theorems in auction theory.
| 14,423 | null |
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] | null |
367 |
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction
|
https://openreview.net/forum?id=sezgRffNiS
|
[
"Shi Yin",
"Xinyang Pan",
"Fengyan Wang",
"Lixin He"
] |
Poster
|
We propose a framework to combine strong non-linear expressiveness with strict SO(3)-equivariance in prediction of the electronic-structure Hamiltonian, by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities and their representations. The proposed framework, called **TraceGrad**, first constructs theoretical SO(3)-invariant **trace** quantities derived from the Hamiltonian targets, and use these invariant quantities as supervisory labels to guide the learning of high-quality SO(3)-invariant features. Given that SO(3)-invariance is preserved under non-linear operations, the learning of invariant features can extensively utilize non-linear mappings, thereby fully capturing the non-linear patterns inherent in physical systems. Building on this, we propose a **grad**ient-based mechanism to induce SO(3)-equivariant encodings of various degrees from the learned SO(3)-invariant features. This mechanism can incorporate powerful non-linear expressive capabilities into SO(3)-equivariant features with correspondence of physical dimensions to the regression targets, while theoretically preserving equivariant properties, establishing a strong foundation for predicting electronic-structure Hamiltonian. Experimental results on eight challenging benchmark databases demonstrate that our method achieves state-of-the-art performance in Hamiltonian prediction.
|
Atomic System Modeling; Electronic-structure Hamiltonian prediction; SO(3)-equivariant representation learning; Non-linear expressiveness
| null | 14,417 | null |
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] | null |
368 |
SlimLLM: Accurate Structured Pruning for Large Language Models
|
https://openreview.net/forum?id=2xjUkU7FDb
|
[
"Jialong Guo",
"Xinghao Chen",
"Yehui Tang",
"Yunhe Wang"
] |
Poster
|
Large language models(LLMs) have garnered significant attention and demonstrated impressive capabilities in a wide range of applications. However, due to their enormous computational costs, the deployment and application of LLMs are often severely limited. To address this issue, structured pruning is an effective solution to compress the parameters of LLMs. Determining the importance of each sub-module in LLMs and minimizing performance loss are critical issues that need to be carefully addressed in structured pruning. In this paper, we propose an effective and fast structured pruning method named SlimLLM for large language models. For channel and attention head pruning, we evaluate the importance based on the entire channel or head, rather than merely aggregating the importance of individual elements within a sub-module. This approach enables a more holistic consideration of the interdependence among elements within the sub-module. In addition, we design a simple linear regression strategy for the output matrix to quickly recover performance. We also propose layer-based importance ratio to determine the pruning ratio for each layer. Based on the LLaMA benchmark results, our SlimLLM outperforms other methods and achieves state-of-the-art performance.
|
structure pruning, large language models, machine learning
| null | 14,414 |
2505.22689
|
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] | null |
369 |
Structure-Guided Large Language Models for Text-to-SQL Generation
|
https://openreview.net/forum?id=gT8JSEFqaS
|
[
"Qinggang Zhang",
"Hao Chen",
"Junnan Dong",
"Shengyuan Chen",
"Feiran Huang",
"Xiao Huang"
] |
Poster
|
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL. However, LLMs often struggle to fully exploit and comprehend the user intention and complex structures of databases. Decomposition-based methods have been proposed to enhance the performance of LLMs on complex tasks, but decomposing SQL generation into subtasks is non-trivial due to the declarative structure of SQL syntax and the intricate connections between query concepts and database elements. In this paper, we propose a novel Structure GUided text-to-SQL framework ( SGU-SQL) that incorporates syntax-based prompting to enhance the SQL generation capabilities of LLMs. Specifically, SGU-SQL establishes structure-aware links between user queries and database schema and recursively decomposes the complex generation task using syntax-based prompting to guide LLMs in incrementally constructing target SQLs. Extensive experiments on two benchmark datasets demonstrate that SGU-SQL consistently outperforms state-of-the-art text-to-SQL baselines.
|
Text-to-SQL, large language model, structure learning
| null | 14,400 | null |
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] | null |
370 |
EGPlace: An Efficient Macro Placement Method via Evolutionary Search with Greedy Repositioning Guided Mutation
|
https://openreview.net/forum?id=5rVcKWyvnt
|
[
"ji deng",
"Zhao Li",
"Ji Zhang",
"Jun Gao"
] |
Poster
|
Macro placement, which involves optimizing the positions of modules, is a critical phase in modern integrated circuit design and significantly influences chip performance. The growing complexity of integrated circuits demands increasingly sophisticated placement solutions. Existing approaches have evolved along two primary paths (e.g., constructive and adjustment methods), but they face significant practical limitations that affect real-world chip design. Recent hybrid frameworks such as WireMask-EA have attempted to combine these strategies, but significant technical barriers still remain, including the computational overhead from separated layout adjustment and reconstruction that often require complete layout rebuilding, the inefficient exploration of design spaces due to random mutation operations, and the computational complexity of mask-based construction methods that limit scalability. To overcome these limitations, we introduce EGPlace, a novel evolutionary optimization framework that combines guided mutation strategies with efficient layout reconstruction. EGPlace introduces two key innovations: a greedy repositioning-guided mutation operator that systematically identifies and optimizes critical layout regions, and an efficient mask computation algorithm that accelerates layout evaluation. Our extensive evaluation using ISPD2005 and Ariane RISC-V CPU benchmarks demonstrate that EGPlace reduces wirelength by \textbf{10.8\%} and \textbf{9.3\%} compared to WireMask-EA and the state-of-the-art reinforcement learning-based constructive method EfficientPlace, respectively, while achieving speedups of 7.8$\times$ and 2.8$\times$ over these methods.
|
Chip Placement, Evolutionary Search, Mutation
| null | 14,395 | null |
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] | null |
371 |
A Trichotomy for List Transductive Online Learning
|
https://openreview.net/forum?id=4gUVnk2Hyo
|
[
"Steve Hanneke",
"Amirreza Shaeiri"
] |
Poster
|
List learning is an important topic in both theoretical and empirical machine learning research, playing a key role in the recent breakthrough result of (Brukhim et al., 2022) on the characterization of multiclass PAC learnability, as well as addressing label ambiguity in computer vision classification tasks, among others. In this paper, we study the problem of list transductive online learning. In this framework, the learner outputs a list of multiple labels for each instance rather than just one, as in traditional multiclass classification. In the realizable setting, we demonstrate a trichotomy of possible rates of the minimax number of mistakes. In particular, if the learner plays for $\text{T} \in \mathbb{N}$ rounds, its minimax number of mistakes can only be of the orders $\Theta(\text{T})$, $\Theta(\log \text{T})$, or $\Theta(1)$. This resolves an open question raised by (Hanneke et al., 2024). On the other hand, in the agnostic setting, we characterize the learnability by constructively proving the $\widetilde{\mathcal{O}}(\sqrt{\text{T}})$ upper bound on the minimax expected regret. Along the way, we also answer another open question asked by (Moran et al., 2023). To establish these results, we introduce two new combinatorial complexity dimensions, called the Level-constrained $(\mathrm{L+1})$-Littlestone dimension and Level-constrained $(\mathrm{L+1})$-Branching dimension, if the list size is $\mathrm{L} \in \mathbb{N}$. Eventually, we conclude our work by raising an open question regarding eliminating the factor list size, which seems to be a crucial step, as it has consistently appeared in previous works on this subject.
|
Online Learning, Transductive Learning, List Learning
|
We study the problem of list transductive online learning when the number of labels can be unbounded.
| 14,383 | null |
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] | null |
372 |
Improving Memory Efficiency for Training KANs via Meta Learning
|
https://openreview.net/forum?id=9biCmI3Mnd
|
[
"Zhangchi Zhao",
"Jun Shu",
"Deyu Meng",
"Zongben Xu"
] |
Poster
|
Inspired by the Kolmogorov-Arnold representation theorem, KANs offer a novel framework for function approximation by replacing traditional neural network weights with learnable univariate functions. This design demonstrates significant potential as an efficient and interpretable alternative to traditional MLPs. However, KANs are characterized by a substantially larger number of trainable parameters, leading to challenges in memory efficiency and higher training costs compared to MLPs. To address this limitation, we propose to generate weights for KANs via a smaller meta-learner, called MetaKANs. By training KANs and MetaKANs in an end-to-end differentiable manner, MetaKANs achieve comparable or even superior performance while significantly reducing the number of trainable parameters and maintaining promising interpretability. Extensive experiments on diverse benchmark tasks, including symbolic regression, partial differential equation solving, and image classification, demonstrate the effectiveness of MetaKANs in improving parameter efficiency and memory usage. The proposed method provides an alternative technique for training KANs, that allows for greater scalability and extensibility, and narrows the training cost gap with MLPs stated in the original paper of KANs. Our code is available at \url{https://github.com/Murphyzc/MetaKAN}.
|
Hypernetwork;Kolmogorov-Arnold networks;Memory efficiency;Scalability and Extensibility
|
We propose an alternative technique for improving scalability and extensibility of KANS via Meta-Learner
| 14,365 |
2506.07549
|
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] |
https://github.com/Murphyzc/MetaKAN
|
373 |
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes
|
https://openreview.net/forum?id=cufJbug7du
|
[
"Ruiqi Zhang",
"Jingfeng Wu",
"Peter Bartlett"
] |
Poster
|
We analyze the convergence of gradient descent (GD) with large, adaptive stepsizes for logistic regression on linearly separable data. The stepsize adapts to the current risk, scaled by a fixed base stepsize \eta. We prove that once the number of iterates t surpasses a margin-dependent threshold, the averaged GD iterate achieves a risk upper bound of \exp(-\Theta(\eta t)), where \eta can be chosen arbitrarily large. This implies that GD attains \emph{arbitrarily fast} convergence rates via large stepsizes, although the risk evolution might not be monotonic. In contrast, prior adaptive stepsize GD analyses require a monotonic risk decrease, limiting their rates to \exp(-\Theta(t)). We further establish a margin-dependent lower bound on the iteration complexity for any first-order method to attain a small risk, justifying the necessity of the burn-in phase in our analysis. Our results generalize to a broad class of loss functions and two-layer networks under additional assumptions.
|
gradient descent, logistic regression, convergence, optimization
|
We prove that gradient descent can converge arbitrarily fast for Logistic Regression on linearly separable data with large and adaptive stepsizes.
| 14,362 | null |
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] | null |
374 |
How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
|
https://openreview.net/forum?id=gVnGcm8E3d
|
[
"Jun Chen",
"Hong Chen",
"Yonghua Yu",
"Yiming Ying"
] |
Poster
|
In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning.
Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely on a default assumption, i.e., the label consistency assumption, which may not hold in practice (the probability of failure is called labeling error) due to the strength and randomness of common augmentation strategies, such as random resized crop (RRC). This paper investigates the theoretical impact of labeling error on the downstream classification performance of contrastive learning. We first reveal several significant negative impacts of labeling error on downstream classification risk. To mitigate these impacts, data dimensionality reduction method (e.g., singular value decomposition, SVD) is applied on original data to reduce false positive samples, and establish both theoretical and empirical evaluations. Moreover, it is also found that SVD acts as a double-edged sword, which may lead to the deterioration of downstream classification accuracy due to the reduced connectivity of the augmentation graph. Based on the above observations, we give the augmentation suggestion that we should use some moderate embedding dimension (such as $512, 1024$ in our experiments), data inflation, weak augmentation, and SVD to ensure large graph connectivity and small labeling error to improve model performance.
|
Contrastive learning, labeling error, data dimensionality reduction, data augmentation
| null | 14,356 | null |
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] | null |
375 |
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
|
https://openreview.net/forum?id=Xsyrolw1Q1
|
[
"Dongwoo Lee",
"Dong Bok Lee",
"Steven Adriaensen",
"Juho Lee",
"Sung Ju Hwang",
"Frank Hutter",
"Seon Joo Kim",
"Hae Beom Lee"
] |
Poster
|
Scaling has been a major driver of recent advancements in deep learning. Numerous empirical studies have found that scaling laws often follow the power-law and proposed several variants of power-law functions to predict the scaling behavior at larger scales. However, existing methods mostly rely on point estimation and do not quantify uncertainty, which is crucial for real-world applications involving decision-making problems such as determining the expected performance improvements achievable by investing additional computational resources. In this work, we explore a Bayesian framework based on Prior-data Fitted Networks (PFNs) for neural scaling law extrapolation. Specifically, we design a prior distribution that enables the sampling of infinitely many synthetic functions resembling real-world neural scaling laws, allowing our PFN to meta-learn the extrapolation. We validate the effectiveness of our approach on real-world neural scaling laws, comparing it against both the existing point estimation methods and Bayesian approaches. Our method demonstrates superior performance, particularly in data-limited scenarios such as Bayesian active learning, underscoring its potential for reliable, uncertainty-aware extrapolation in practical applications.
|
Neural Scaling Laws, Bayesian Inference, Prior-data Fitted Networks
|
We propose a novel Bayesian neural scaling law extrapolation method based on Prior-data Fitted Networks.
| 14,355 |
2505.23032
|
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] |
https://github.com/DongWooLee-Eli/nslpfn
|
376 |
Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective
|
https://openreview.net/forum?id=biMO2gWsWS
|
[
"Qingchuan Ma",
"Yuhang Wu",
"Xiawu Zheng",
"Rongrong Ji"
] |
Poster
|
In this paper, we aim to establish a simple, effective, and theoretically grounded benchmark for rigorously probing abstract reasoning in Large Language Models (LLMs). To achieve this, we first develop a mathematic framework that defines abstract reasoning as the ability to: (i) extract essential patterns independent of surface representations, and (ii) apply consistent rules to these abstract patterns. Based on this framework, we introduce two novel complementary metrics: Γ measures basic reasoning accuracy, while ∆ quantifies a model's reliance on specific symbols rather than underlying patterns - a key indicator of true abstraction versus mere memorization. To implement this measurement, we design a benchmark: systematic symbol remapping in rule-based tasks, which forces models to demonstrate genuine pattern recognition beyond superficial token matching. Extensive LLM evaluations using this benchmark (commercial API models, 7B-70B, multi-agent) reveal:1) critical limitations in non-decimal arithmetic and symbolic reasoning; 2) persistent abstraction gaps despite chain-of-thought prompting; and 3) ∆'s effectiveness in robustly measuring memory dependence by quantifying performance degradation under symbol remapping, particularly highlighting operand-specific memorization. These findings underscore that current LLMs, despite domain-specific strengths, still lack robust abstract reasoning, highlighting key areas for future improvement.
|
Large Language Models, Abstract Reasoning, Benchmark
| null | 14,347 |
2505.23833
|
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] |
https://github.com/MAC-AutoML/abstract-reason-benchmark
|
377 |
Online Conformal Prediction via Online Optimization
|
https://openreview.net/forum?id=KwGc2JUIDK
|
[
"Felipe Areces",
"Christopher Mohri",
"Tatsunori Hashimoto",
"John Duchi"
] |
Poster
|
We introduce a family of algorithms for online conformal prediction with coverage guarantees for both adversarial and stochastic data. In the adversarial setting, we establish the standard guarantee: over time, a pre-specified target fraction of confidence sets cover the ground truth. For stochastic data, we provide a guarantee at every time instead of just on average over time: the probability that a confidence set covers the ground truth—conditioned on past observations—converges to a pre-specified target when the conditional quantiles of the errors are a linear function of past data. Complementary to our theory, our experiments spanning over $15$ datasets suggest that the performance improvement of our methods over baselines grows with the magnitude of the data’s dependence, even when baselines are tuned on the test set. We put these findings to the test by pre-registering an experiment for electricity demand forecasting in Texas, where our algorithms achieve over a $10$\% reduction in confidence set sizes, a more than a $30$\% improvement in quantile and absolute losses with respect to the observed errors, and significant outcomes on all $78$ out of $78$ pre-registered hypotheses. We provide documentation for the pypi package implementing our algorithms here: \url{https://conformalopt.readthedocs.io/}.
|
conformal prediction, online conformal prediction, stochastic convex optimization, stochastic gradient descent, uncertainty quantification
|
We introduce a family of algorithms for online conformal prediction with coverage guarantees for both adversarial and stochastic data.
| 14,344 | null |
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] | null |
378 |
When Bad Data Leads to Good Models
|
https://openreview.net/forum?id=SsLGTZKXf1
|
[
"Kenneth Li",
"Yida Chen",
"Fernanda Viégas",
"Martin Wattenberg"
] |
Poster
|
In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.
|
Large Language Models, Toxicity
|
A little bit of toxic data in pretraining can act as a catalyst for more alignable language models.
| 14,342 |
2505.04741
|
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] | null |
379 |
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
|
https://openreview.net/forum?id=51tMpvPNSm
|
[
"Jiancong Xiao",
"Bojian Hou",
"Zhanliang Wang",
"Ruochen Jin",
"Qi Long",
"Weijie J Su",
"Li Shen"
] |
Poster
|
One of the key technologies for the success of Large Language Models (LLMs) is preference alignment. However, a notable side effect of preference alignment is poor calibration: while the pre-trained models are typically well-calibrated, LLMs tend to become poorly calibrated after alignment with human preferences. In this paper, we investigate why preference alignment affects calibration and how to address this issue. For the first question, we observe that the preference collapse issue in alignment undesirably generalizes to the calibration scenario, causing LLMs to exhibit overconfidence and poor calibration. To address this, we demonstrate the importance of fine-tuning
with domain-specific knowledge to alleviate the overconfidence issue. To further analyze whether this affects the model's performance, we categorize models into two regimes: calibratable and non-calibratable, defined by bounds of Expected Calibration Error (ECE). In the calibratable regime, we propose a calibration-aware fine-tuning approach to achieve proper calibration without compromising LLMs' performance. However, as models are further fine-tuned for better performance, they enter the non-calibratable regime. For this case, we develop an EM-algorithm-based ECE regularization for the fine-tuning loss to maintain low calibration error. Extensive experiments validate the effectiveness of the proposed methods.
|
Large Language Models; Calibration; Fine-Tuning
|
An approach for restoring calibration in LLMs.
| 14,329 |
2505.01997
|
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] | null |
380 |
Rethinking Point Cloud Data Augmentation: Topologically Consistent Deformation
|
https://openreview.net/forum?id=oDPtv1RveE
|
[
"Jian Bi",
"Qianliang Wu",
"Xiang Li",
"Shuo Chen",
"Jianjun Qian",
"lei luo",
"Jian Yang"
] |
Poster
|
Data augmentation has been widely used in machine learning. Its main goal is to transform and expand the original data using various techniques, creating a more diverse and enriched training dataset. However, due to the disorder and irregularity of point clouds, existing methods struggle to enrich geometric diversity and maintain topological consistency, leading to imprecise point cloud understanding. In this paper, we propose SinPoint, a novel method designed to preserve the topological structure of the original point cloud through a homeomorphism. It utilizes the Sine function to generate smooth displacements. This simulates object deformations, thereby producing a rich diversity of samples. In addition, we propose a Markov chain Augmentation Process to further expand the data distribution by combining different basic transformations through a random process. Our extensive experiments demonstrate that our method consistently outperforms existing Mixup and Deformation methods on various benchmark point cloud datasets, improving performance for shape classification and part segmentation tasks. Specifically, when used with PointNet++ and DGCNN, our method achieves a state-of-the-art accuracy of 90.2 in shape classification with the real-world ScanObjectNN dataset. We release the code at https://github.com/CSBJian/SinPoint.
|
3D Point Cloud, Homeomorphism, Data Augmentation
|
A simple and effective point cloud augmentation method based on homeomorphism
| 14,321 | null |
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] | null |
381 |
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
|
https://openreview.net/forum?id=CgJEHynkJt
|
[
"Yilun Zhou",
"Austin Xu",
"PeiFeng Wang",
"Caiming Xiong",
"Shafiq Joty"
] |
Poster
|
Scaling test-time computation, or affording a generator large language model (LLM) extra compute during inference, typically employs the help of external non-generative evaluators (i.e., reward models). Concurrently, LLM-judges, models trained to generate evaluations and critiques (explanations) in natural language, are becoming increasingly popular in automatic evaluation. Despite judge empirical successes, their effectiveness as evaluators in test-time scaling settings is largely unknown. In this paper, we introduce the Judge Evaluation for Test-Time Scaling (JETTS) benchmark, which evaluates judge performance in three domains (math reasoning, code generation, and instruction following) under three task settings: response reranking, step-level beam search, and critique-based response refinement. We evaluate 10 different judge models (7B-70B parameters) for 8 different base generator models (6.7B-72B parameters). Our benchmark shows that while judges are competitive with outcome reward models in reranking, they are consistently worse than process reward models in beam search procedures. Furthermore, though unique to LLM-judges, their natural language critiques are currently ineffective in guiding the generator towards better responses.
|
LLM-as-judge, test-time scaling, benchmark
|
We propose a novel benchmark, JETTS, to evaluate LLM-as-judges from the perspective of their helpfulness as evaluators for generator's test-time scaling.
| 14,314 |
2504.15253
|
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] |
https://github.com/salesforceairesearch/jetts-benchmark
|
382 |
Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models
|
https://openreview.net/forum?id=dfOqiHuklY
|
[
"Haoyu Peter Wang",
"Shikun Liu",
"Rongzhe Wei",
"Pan Li"
] |
Poster
|
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce. Among these applications, inference over text-attributed graphs (TAGs) presents unique challenges: existing methods struggle with LLMs' limited context length for processing large node neighborhoods and the misalignment between node embeddings and the LLM token space. To address these issues, we establish two key principles for ensuring generalization and derive the framework LLM-BP accordingly: (1) **Unifying the attribute space with task-adaptive embeddings**, where we leverage LLM-based encoders and task-aware prompting to enhance generalization of the text attribute embeddings; (2) **Developing a generalizable graph information aggregation mechanism**, for which we adopt belief propagation with LLM-estimated parameters that adapt across graphs. Evaluations on 11 real-world TAG benchmarks demonstrate that LLM-BP significantly outperforms existing approaches, achieving 8.10\% improvement with task-conditional embeddings and an additional 1.71\% gain from adaptive aggregation. The code and task-adaptive embeddings are publicly available.
|
text-attributed graphs, zero-shot learning, large language model
| null | 14,296 | null |
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] |
https://github.com/Graph-COM/LLM_BP
|
383 |
Sample-Optimal Agnostic Boosting with Unlabeled Data
|
https://openreview.net/forum?id=hcLeFe7idT
|
[
"Udaya Ghai",
"Karan Singh"
] |
Poster
|
Boosting provides a practical and provably effective framework for constructing accurate learning algorithms from inaccurate rules of thumb. It extends the promise of sample-efficient learning to settings where direct Empirical Risk Minimization (ERM) may not be implementable efficiently. In the realizable setting, boosting is known to offer this computational reprieve without compromising on sample efficiency. However, in the agnostic case, existing boosting algorithms fall short of achieving the optimal sample complexity.
We highlight a previously unexplored avenue of improvement: unlabeled samples. We design a computationally efficient agnostic boosting algorithm that matches the sample complexity of ERM, given polynomially many additional unlabeled samples. In fact, we show that the total number of samples needed, unlabeled and labeled inclusive, is never more than that for the best known agnostic boosting algorithm -- so this result is never worse -- while only a vanishing fraction of these need to be labeled for the algorithm to succeed. This is particularly fortuitous for learning-theoretic applications of agnostic boosting, which often take place in the distribution-specific setting, where unlabeled samples can be availed for free. We also prove that the resultant guarantee is resilient against mismatch between the distributions governing the labeled and unlabeled samples. Finally, we detail an application of this result in reinforcement learning.
|
boosting, agnostic learning, weak learning, sample complexity, semi supervised
|
Agnostic Boosting algorithms with sample complexity matching that of ERM, given additional unlabeled data (often this is free!)
| 14,289 |
2503.04706
|
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] | null |
384 |
BiMark: Unbiased Multilayer Watermarking for Large Language Models
|
https://openreview.net/forum?id=Zvyb3WAg03
|
[
"Xiaoyan Feng",
"He Zhang",
"Yanjun Zhang",
"Leo Yu Zhang",
"Shirui Pan"
] |
Poster
|
Recent advances in Large Language Models (LLMs) have raised urgent concerns about LLM-generated text authenticity, prompting regulatory demands for reliable identification mechanisms.
Although watermarking offers a promising solution, existing approaches struggle to simultaneously achieve three critical requirements: text quality preservation, model-agnostic detection, and message embedding capacity, which are crucial for practical implementation.
To achieve these goals, the key challenge lies in balancing the trade-off between text quality preservation and message embedding capacity.
To address this challenge, we propose BiMark, a novel watermarking framework that achieves these requirements through three key innovations:
(1) a bit-flip unbiased reweighting mechanism enabling model-agnostic detection, (2) a multilayer architecture enhancing detectability without compromising generation quality, and (3) an information encoding approach supporting multi-bit watermarking.
Through theoretical analysis and extensive experiments, we validate that,
compared to state-of-the-art multi-bit watermarking methods, BiMark achieves up to 30\% higher extraction rates for short texts while maintaining text quality indicated by lower perplexity, and performs comparably to non-watermarked text on downstream tasks such as summarization and translation.
|
Language Model Watermarking, Text Generation, Model Security
| null | 14,283 |
2506.21602
|
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] | null |
385 |
MedRAX: Medical Reasoning Agent for Chest X-ray
|
https://openreview.net/forum?id=JiFfij5iv0
|
[
"Adibvafa Fallahpour",
"Jun Ma",
"Alif Munim",
"Hongwei Lyu",
"BO WANG"
] |
Poster
|
Chest X-rays (CXRs) play an integral role in driving critical decisions in disease management and patient care. While recent innovations have led to specialized models for various CXR interpretation tasks, these solutions often operate in isolation, limiting their practical utility in clinical practice. We present MedRAX, the first versatile AI agent that seamlessly integrates state-of-the-art CXR analysis tools and multimodal large language models into a unified framework. MedRAX dynamically leverages these models to address complex medical queries without requiring additional training. To rigorously evaluate its capabilities, we introduce ChestAgentBench, a comprehensive benchmark containing 2,500 complex medical queries across 7 diverse categories. Our experiments demonstrate that MedRAX achieves state-of-the-art performance compared to both open-source and proprietary models, representing a significant step toward the practical deployment of automated CXR interpretation systems. Data and code have been publicly available at https://github.com/bowang-lab/MedRAX
|
healthcare, agent, multimodal, chest X-ray, benchmark
|
We introduce MedRAX, a state-of-the-art AI agent for chest X-ray interpretation.
| 14,282 | null |
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] |
https://github.com/bowang-lab/MedRAX
|
386 |
Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF
|
https://openreview.net/forum?id=xHhURhYmgK
|
[
"Nuoya Xiong",
"Aarti Singh"
] |
Poster
|
Reinforcement Learning with Human Feedback (RLHF) is a widely used fine-tuning approach that aligns machine learning models, particularly Language Models (LMs) with human preferences. There are typically multiple objectives driving the preference, hence humans find it easier to express per-objective comparisons rather than a global preference between two choices, e.g. compare two papers on their novelty, clarity, correctness, etc. Multi-Objective RLHF aims to use per-objective preference feedback and achieve a Pareto optimal tradeoff among these objectives by aggregating them into a single unified objective for optimization. However, nearly all prior works rely on linear aggregation, which rules out policies that favor specific objectives such as the worst one. The only existing approach using non-linear aggregation is computationally expensive due to its reward-based nature and the need for retraining whenever the aggregation parameters change. In this work, we address this limitation by transforming the non-linear aggregation maximization problem into a series of sub-problems. Each sub-problem involves only linear aggregation, making it computationally efficient to solve. We further extend our framework to handle multi-group scenarios, where each group has distinct weights for the objectives. Our method enables achieving consensus or maximizing the aggregated objective across all groups. Theoretically, we demonstrate that our algorithmic framework achieves sublinear regret and can be easily adapted to a reward-free algorithm. Empirically, leveraging our theoretical insights, we propose a nearly training-free algorithm once the optimal policies for individual objectives are obtained.
|
multi-objective learning, RLHF theory
|
We provide a reward-free algorithmic framework for multi-objective and multi-group RLHF with general non-linear aggregation.
| 14,279 |
2502.15145
|
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] | null |
387 |
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
|
https://openreview.net/forum?id=HMequFD3Uz
|
[
"Joseph Lazzaro",
"Ciara Pike-Burke"
] |
Poster
|
Piecewise constant functions describe a variety of real-world phenomena in domains ranging from chemistry to manufacturing. In practice, it is often required to confidently identify the locations of the abrupt changes in these functions as quickly as possible. For this, we introduce a fixed-confidence piecewise constant bandit problem. Here, we sequentially query points in the domain and receive noisy evaluations of the function under bandit feedback. We provide instance-dependent lower bounds for the complexity of change point identification in this problem. These lower bounds illustrate that an optimal method should focus its sampling efforts adjacent to each of the change points, and the number of samples around each change point should be inversely proportional to the magnitude of the change.
Building on this, we devise a simple and computationally efficient variant of Track-and-Stop and prove that it is asymptotically optimal in many regimes. We support our theoretical findings with experimental results in synthetic environments demonstrating the efficiency of our method.
|
bandits, pure exploration, fixed confidence, change points
| null | 14,275 |
2507.08994
|
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] | null |
388 |
Implicit Subgraph Neural Network
|
https://openreview.net/forum?id=QhCb3FAQi2
|
[
"Yongjian Zhong",
"Liao Zhu",
"Hieu Vu",
"Bijaya Adhikari"
] |
Poster
|
Subgraph neural networks have recently gained prominence for various subgraph-level predictive tasks. However, existing methods either \emph{1)} apply simple standard pooling over graph convolutional networks, failing to capture essential subgraph properties, or \emph{2)} rely on rigid subgraph definitions, leading to suboptimal performance. Moreover, these approaches fail to model long-range dependencies both between and within subgraphs—a critical limitation, as many real-world networks contain subgraphs of varying sizes and connectivity patterns.
In this paper, we propose a novel implicit subgraph neural network, the first of its kind, designed to capture dependencies across subgraphs. Our approach also integrates label-aware subgraph-level information. We formulate implicit subgraph learning as a bilevel optimization problem and develop a provably convergent algorithm that requires fewer gradient estimations than standard bilevel optimization methods.
We evaluate our approach on real-world networks against state-of-the-art baselines, demonstrating its effectiveness and superiority.
|
Subgraph Classification, Implicit Models
| null | 14,271 | null |
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] |
https://github.com/MLonGraph/ISNN
|
389 |
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
|
https://openreview.net/forum?id=CAvnZQgrLu
|
[
"Taehyun Cho",
"Seungyub Han",
"Seokhun Ju",
"Dohyeong Kim",
"Kyungjae Lee",
"Jungwoo Lee"
] |
Poster
|
Distributional reinforcement learning improves performance by capturing environmental stochasticity, but a comprehensive theoretical understanding of its effectiveness remains elusive.
In addition, the intractable element of the infinite dimensionality of distributions has been overlooked.
In this paper, we present a regret analysis of distributional reinforcement learning with general value function approximation in a finite episodic Markov decision process setting.
We first introduce a key notion of Bellman unbiasedness which is essential for exactly learnable and provably efficient distributional updates in an online manner.
Among all types of statistical functionals for representing infinite-dimensional return distributions, our theoretical results demonstrate that only moment functionals can exactly capture the statistical information.
Secondly, we propose a provably efficient algorithm, SF-LSVI, that achieves a tight regret bound of $\tilde{O}(d_E H^{\frac{3}{2}}\sqrt{K})$ where $H$ is the horizon, $K$ is the number of episodes, and $d_E$ is the eluder dimension of a function class.
|
Distributional RL, Regret Minimization
| null | 14,259 |
2407.21260
|
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] | null |
390 |
Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting
|
https://openreview.net/forum?id=khFk7sdv9o
|
[
"Can Chen",
"Jun-Kun Wang"
] |
Poster
|
Developing algorithms to differentiate between machine-generated texts and human-written texts has garnered substantial attention in recent years. Existing methods in this direction typically concern an offline setting where a dataset containing a mix of real and machine-generated texts is given upfront, and the task is to determine whether each sample in the dataset is from a large language model (LLM) or a human. However, in many practical scenarios, sources such as news websites, social media accounts, and online forums publish content in a streaming fashion. Therefore, in this online scenario, how to quickly and accurately determine whether the source is an LLM with strong statistical guarantees is crucial for these media or platforms to function effectively and prevent the spread of misinformation and other potential misuse of LLMs. To tackle the problem of online detection, we develop an algorithm based on the techniques of sequential hypothesis testing by betting that not only builds upon and complements existing offline detection techniques but also enjoys statistical guarantees, which include a controlled false positive rate and the expected time to correctly identify a source as an LLM. Experiments were conducted to demonstrate the effectiveness of our method.
|
LLM-Generated Text, Text Source Detection, Sequential Hypothesis Testing, Online Optimization
| null | 14,256 |
2410.22318
|
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] |
https://github.com/canchen-cc/online-llm-detection
|
391 |
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
|
https://openreview.net/forum?id=BdQKHcrBxT
|
[
"Zijian Liu",
"Zhengyuan Zhou"
] |
Poster
|
We study the convergence of the shuffling gradient method, a popular algorithm employed to minimize the finite-sum function with regularization, in which functions are passed to apply (Proximal) Gradient Descent (GD) one by one whose order is determined by a permutation on the indices of functions. In contrast to its easy implementation and effective performance in practice, the theoretical understanding remains limited. A recent advance by (Liu & Zhou, 2024b) establishes the first last-iterate convergence results under various settings, especially proving the optimal rates for smooth (strongly) convex optimization. However, their bounds for nonsmooth (strongly) convex functions are only as fast as Proximal GD. In this work, we provide the first improved last-iterate analysis for the nonsmooth case demonstrating that the widely used Random Reshuffle ($\textsf{RR}$) and Single Shuffle ($\textsf{SS}$) strategies are both provably faster than Proximal GD, reflecting the benefit of randomness. As an important implication, we give the first (nearly) optimal convergence result for the suffix average under the $\textsf{RR}$ sampling scheme in the general convex case, matching the lower bound shown by (Koren et al., 2022).
|
Convex optimizaton, Nonsmooth optimization, Shuffling methods
| null | 14,232 |
2505.23056
|
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] | null |
392 |
EFDTR: Learnable Elliptical Fourier Descriptor Transformer for Instance Segmentation
|
https://openreview.net/forum?id=nSldadGGY5
|
[
"Jiawei Cao",
"Chaochen Gu",
"Hao Cheng",
"Xiaofeng Zhang",
"Kaijie Wu",
"Changsheng Lu"
] |
Poster
|
Polygon-based object representations efficiently model object boundaries but are limited by high optimization complexity, which hinders their adoption compared to more flexible pixel-based methods.
In this paper, we introduce a novel vertex regression loss grounded in Fourier elliptic descriptors, which removes the need for rasterization or heuristic approximations and resolves ambiguities in boundary point assignment through frequency-domain matching.
To advance polygon-based instance segmentation, we further propose EFDTR (\textbf{E}lliptical \textbf{F}ourier \textbf{D}escriptor \textbf{Tr}ansformer), an end-to-end learnable framework that leverages the expressiveness of Fourier-based representations.
The model achieves precise contour predictions through a two-stage approach: the first stage predicts elliptical Fourier descriptors for global contour modeling, while the second stage refines contours for fine-grained accuracy. Experimental results on the COCO dataset show that EFDTR outperforms existing polygon-based methods, offering a promising alternative to pixel-based approaches. Code is available at \url{https://github.com/chrisclear3/EFDTR}.
|
Instance Segmentation, Learnable Elliptical Fourier Descriptor, Contour Prediction
|
EFDTR: A polygon-based instance segmentation framework using Elliptic Fourier Descriptors (EFDs) for precise contour modeling, outperforming polygon methods and rivaling mask-based approaches.
| 14,224 | null |
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] |
https://github.com/chrisclear3/EFDTR
|
393 |
TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization
|
https://openreview.net/forum?id=TKHWvyzR1t
|
[
"Mingkang Zhu",
"Xi Chen",
"Zhongdao Wang",
"Bei Yu",
"Hengshuang Zhao",
"Jiaya Jia"
] |
Poster
|
Recent advancements in reinforcement learning from human feedback have shown that utilizing fine-grained token-level reward models can substantially enhance the performance of Proximal Policy Optimization (PPO) in aligning large language models. However, it is challenging to leverage such token-level reward as guidance for Direct Preference Optimization (DPO), since DPO is formulated as a sequence-level bandit problem. To address this challenge, this work decomposes the sequence-level PPO into a sequence of token-level proximal policy optimization problems and then frames the problem of token-level PPO with token-level reward guidance, from which closed-form optimal token-level policy and the corresponding token-level reward can be derived. Using the obtained reward and Bradley-Terry model, this work establishes a framework of computable loss functions with token-level reward guidance for DPO, and proposes a practical reward guidance based on the induced DPO reward. This formulation enables different tokens to exhibit varying degrees of deviation from reference policy based on their respective rewards. Experiment results demonstrate that our method achieves substantial performance improvements over DPO, with win rate gains of up to 7.5 points on MT-Bench, 6.2 points on AlpacaEval 2, and 4.3 points on Arena-Hard. Code is available at https://github.com/dvlab-research/TGDPO.
|
Large Language Models, Preference Optimization
| null | 14,203 |
2506.14574
|
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] |
https://github.com/dvlab-research/TGDPO
|
394 |
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
|
https://openreview.net/forum?id=wl3eI4wiE5
|
[
"Eric Zhao",
"Pranjal Awasthi",
"Sreenivas Gollapudi"
] |
Poster
|
Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one---typically by verifying each response for correctness. In this paper, we study the scaling trends governing sampling-based search. Among our findings is that simply scaling up a minimalist implementation that uses only random sampling and direct self-verification results in sustained performance improvements that, for example, elevate the Gemini v1.5 Pro model's reasoning capabilities past that of o1-Preview on popular benchmarks. We partially attribute the scalability of sampling-based search to a phenomenon of implicit scaling, where sampling a larger pool of responses in turn improves verification accuracy. We further identify two useful principles for improving self-verification capabilities with test-time compute: (1) comparing across responses provides helpful signals about the locations of errors and hallucinations, and (2) different model output styles are useful for different contexts---chains of thought are useful for reasoning but harder to verify. We also find that, though accurate verification can be elicited, frontier models demonstrate remarkably weak out-of-box verification capabilities and introduce a benchmark to measure progress on these deficiencies.
|
reasoning, search, verification
|
We study the scaling trends governing sampling-based search, a test-time compute scaling paradigm.
| 14,201 |
2502.01839
|
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] | null |
395 |
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
|
https://openreview.net/forum?id=MM6ZWF7gl9
|
[
"Xin Yu",
"Zelin He",
"Ying Sun",
"Lingzhou Xue",
"Runze Li"
] |
Poster
|
Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offer theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.
|
Personalized Federated Learning, Statistical Complexity, Communication Complexity
| null | 14,197 |
2410.08934
|
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] |
https://github.com/ZLHe0/fedclup
|
396 |
Scaling Laws for Differentially Private Language Models
|
https://openreview.net/forum?id=DE6dqmcmQ9
|
[
"Ryan McKenna",
"Yangsibo Huang",
"Amer Sinha",
"Borja Balle",
"Zachary Charles",
"Christopher A. Choquette-Choo",
"Badih Ghazi",
"Georgios Kaissis",
"Ravi Kumar",
"Ruibo Liu",
"Da Yu",
"Chiyuan Zhang"
] |
Poster
|
Scaling laws have emerged as important components of large language model (LLM) training as they can predict performance gains through scale, and provide guidance on important hyper-parameter choices that would otherwise be expensive. LLMs also rely on large, high-quality training datasets, like those sourced from (sometimes sensitive) user data. Training models on this sensitive user data requires careful privacy protections like differential privacy (DP). However, the dynamics of DP training are significantly different, and consequently their scaling laws are not yet fully understood. In this work, we establish scaling laws that accurately model the intricacies of DP LLM training, providing a complete picture of the compute-privacy-utility and the optimal training configurations in many settings.
|
scaling laws, differential privacy
|
We aim to understand and quantify the compute-privacy-utility trade-offs in language models training
| 14,196 |
2501.18914
|
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] |
https://github.com/google-research/google-research/tree/master/dp_scaling_laws
|
397 |
Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms
|
https://openreview.net/forum?id=mvbWw0w7pG
|
[
"Kei Sen Fong",
"Mehul Motani"
] |
Poster
|
Symbolic Regression (SR) algorithms select expressions based on prediction performance while also keeping the expression lengths short to produce explainable white box models. In this context, SR algorithms can be evaluated by measuring the extent to which the expressions discovered are Pareto-optimal, in the sense of having the best R-squared score for a given expression length. This evaluation is most commonly done based on relative performance, in the sense that an SR algorithm is judged on whether it Pareto-dominates other SR algorithms selected in the analysis, without any indication on efficiency or attainable limits. In this paper, we explore absolute Pareto-optimal (APO) solutions instead, which have the optimal tradeoff between the multiple SR objectives, for 34 datasets in the widely-used SR benchmark, SRBench, by performing exhaustive search. Additionally, we include comparisons between eight numerical optimization methods. We extract, for every dataset, an APO front of expressions that can serve as a universal baseline for SR algorithms that informs researchers of the best attainable performance for selected sizes. The APO fronts provided serves as an important benchmark and performance limit for SR algorithms and is made publicly available at: https://github.com/kentridgeai/SRParetoFronts
|
Symbolic Regression, Benchmark
| null | 14,182 | null |
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] |
https://github.com/kentridgeai/SRParetoFronts
|
398 |
Faster Global Minimum Cut with Predictions
|
https://openreview.net/forum?id=FeZoO7mfG7
|
[
"Helia Niaparast",
"Benjamin Moseley",
"Karan Singh"
] |
Poster
|
Global minimum cut is a fundamental combinatorial optimization problem with wide-ranging applications. Often in practice, these problems are solved repeatedly on families of similar or related instances. However, the de facto algorithmic approach is to solve each instance of the problem from scratch discarding information from prior instances.
In this paper, we consider how predictions informed by prior instances can be used to warm-start practical minimum cut algorithms. The paper considers the widely used Karger's algorithm and its counterpart, the Karger-Stein algorithm. Given good predictions, we show these algorithms become near-linear time and have robust performance to erroneous predictions. Both of these algorithms are randomized edge-contraction algorithms. Our natural idea is to probabilistically prioritize the contraction of edges that are unlikely to be in the minimum cut.
|
Algorithms with Predictions, Minimum Cut, Karger, Karger-Stein
|
We incorporate predictions into Karger and Karger-Stein algorithms to develop faster methods for finding the global minimum cut.
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2503.05004
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] |
https://github.com/helia-niaparast/global-minimum-cut-with-predictions
|
399 |
FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation
|
https://openreview.net/forum?id=ApVH0G4l6P
|
[
"Kei Sen Fong",
"Mehul Motani"
] |
Poster
|
In this work, we propose a novel approach that combines the strengths of FEAT and TabNet through knowledge distillation (KD), which we term FEAT-KD. FEAT is an intrinsically interpretable machine learning (ML) algorithm that constructs a weighted linear combination of concisely-represented features discovered via genetic programming optimization, which can often be inefficient. FEAT-KD leverages TabNet's deep-learning-based optimization and feature selection mechanisms instead. FEAT-KD finds a weighted linear combination of concisely-represented, symbolic features that are derived from piece-wise distillation of a trained TabNet model. We analyze FEAT-KD on regression tasks from two perspectives:
(i) compared to TabNet, FEAT-KD significantly reduces model complexity while retaining competitive predictive performance, effectively converting a black-box deep learning model into a more interpretable white-box representation, (ii) compared to FEAT, our method consistently outperforms in prediction accuracy, produces more compact models, and reduces the complexity of learned symbolic expressions. In addition, we demonstrate that FEAT-KD easily supports multi-target regression, in which the shared features contribute to the interpretability of the system. Our results suggest that FEAT-KD is a promising direction for interpretable ML, bridging the gap between deep learning's predictive power and the intrinsic transparency of symbolic models.
|
FEAT, Knowledge Distillation, Symbolic Regression
| null | 14,161 | null |
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] | null |
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