Modeling the polygenic architecture of complex traits
Rakitsch, Barbara
Eberhard Karls Universität Tübingen, November 2014 (phdthesis)
ei Rakitsch, Barbara Modeling the polygenic architecture of complex traits Eberhard Karls Universität Tübingen, November 2014 (phdthesis)
Method and device for blind correction of optical aberrations in a digital image
Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.
International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)
ei Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B. Method and device for blind correction of optical aberrations in a digital image International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)
A Novel Causal Inference Method for Time Series
Shajarisales, N.
Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)
ei Shajarisales, N. A Novel Causal Inference Method for Time Series Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)
A global analysis of extreme events and consequences for the terrestrial carbon cycle
Zscheischler, J.
Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)
ei Zscheischler, J. A global analysis of extreme events and consequences for the terrestrial carbon cycle Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)
Simultaneous Whole-Body PET/MR Imaging in Comparison to PET/CT in Pediatric Oncology: Initial Results
Schäfer, J. F., Gatidis, S., Schmidt, H., Gückel, B., Bezrukov, I., Pfannenberg, C. A., Reimold, M., M., E., Fuchs, J., Claussen, C. D., Schwenzer, N. F.
Radiology, 273(1):220-231, 2014 (article)
ei Schäfer, J. F., Gatidis, S., Schmidt, H., Gückel, B., Bezrukov, I., Pfannenberg, C. A., Reimold, M., M., E., Fuchs, J., Claussen, C. D., Schwenzer, N. F. Simultaneous Whole-Body PET/MR Imaging in Comparison to PET/CT in Pediatric Oncology: Initial Results Radiology, 273(1):220-231, 2014 (article)
Development of advanced methods for improving astronomical images
Schmeißer, N.
Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)
ei Schmeißer, N. Development of advanced methods for improving astronomical images Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)
The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection
Lacosse, E.
Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)
ei Lacosse, E. The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)
Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification
Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Naesset, E., Schölkopf, B.
IEEE Transactions on Geoscience and Remote Sensing, 10(52):6652 - 6664, 2014 (article)
ei Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Naesset, E., Schölkopf, B. Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification IEEE Transactions on Geoscience and Remote Sensing, 10(52):6652 - 6664, 2014 (article)
Modular proximal optimization with application to total variation regularization
Barbero, A. J., Sra, S.
2014 (article) In revision
ei Barbero, A. J., Sra, S. Modular proximal optimization with application to total variation regularization 2014 (article) In revision
Principles of PET/MR Imaging
Disselhorst, J. A., Bezrukov, I., Kolb, A., Parl, C., Pichler, B. J.
Journal of Nuclear Medicine, 55(6, Supplement 2):2S-10S, 2014 (article)
ei Disselhorst, J. A., Bezrukov, I., Kolb, A., Parl, C., Pichler, B. J. Principles of PET/MR Imaging Journal of Nuclear Medicine, 55(6, Supplement 2):2S-10S, 2014 (article)
IM3SHAPE: Maximum likelihood galaxy shear measurement code for cosmic gravitational lensing
Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S.
Astrophysics Source Code Library, 1, pages: 09013, 2014 (article)
ei Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S. IM3SHAPE: Maximum likelihood galaxy shear measurement code for cosmic gravitational lensing Astrophysics Source Code Library, 1, pages: 09013, 2014 (article)
Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size
Huang, B.
ei Huang, B. Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)
Efficient nearest neighbors via robust sparse hashing
Cherian, A., Sra, S., Morellas, V., Papanikolopoulos, N.
IEEE Transactions on Image Processing, 23(8):3646-3655, 2014 (article)
ei Cherian, A., Sra, S., Morellas, V., Papanikolopoulos, N. Efficient nearest neighbors via robust sparse hashing IEEE Transactions on Image Processing, 23(8):3646-3655, 2014 (article)
Sérsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction
Kacprzak, T., Bridle, S., Rowe, B., Voigt, L., Zuntz, J., Hirsch, M., MacCrann, N.
Monthly Notices of the Royal Astronomical Society, 441(3):2528-2538, Oxford University Press, 2014 (article)
ei Kacprzak, T., Bridle, S., Rowe, B., Voigt, L., Zuntz, J., Hirsch, M., MacCrann, N. Sérsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction Monthly Notices of the Royal Astronomical Society, 441(3):2528-2538, Oxford University Press, 2014 (article)
Analysis of Distance Functions in Graphs
Alamgir, M.
University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)
ei Alamgir, M. Analysis of Distance Functions in Graphs University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)
Diminished White Matter Integrity in Patients with Systemic Lupus Erythematosus
Schmidt-Wilcke, T., Cagnoli, P., Wang, P., Schultz, T., Lotz, A., Mccune, W. J., Sundgren, P. C.
NeuroImage: Clinical, 5, pages: 291-297, 2014 (article)
ei Schmidt-Wilcke, T., Cagnoli, P., Wang, P., Schultz, T., Lotz, A., Mccune, W. J., Sundgren, P. C. Diminished White Matter Integrity in Patients with Systemic Lupus Erythematosus NeuroImage: Clinical, 5, pages: 291-297, 2014 (article)
Information-Theoretic Bounded Rationality and ϵ-Optimality
Braun, DA, Ortega, PA
Entropy, 16(8):4662-4676, August 2014 (article)
Bounded rationality concerns the study of decision makers with limited information processing resources. Previously, the free energy difference functional has been suggested to model bounded rational decision making, as it provides a natural trade-off between an energy or utility function that is to be optimized and information processing costs that are measured by entropic search costs. The main question of this article is how the information-theoretic free energy model relates to simple \(\epsilon\)-optimality models of bounded rational decision making, where the decision maker is satisfied with any action in an \(\epsilon\)-neighborhood of the optimal utility. We find that the stochastic policies that optimize the free energy trade-off comply with the notion of \(\epsilon\)-optimality. Moreover, this optimality criterion even holds when the environment is adversarial. We conclude that the study of bounded rationality based on \(\epsilon\)-optimality criteria that abstract away from the particulars of the information processing constraints is compatible with the information-theoretic free energy model of bounded rationality.
ei Braun, DA, Ortega, PA Information-Theoretic Bounded Rationality and ϵ-Optimality Entropy, 16(8):4662-4676, August 2014 (article)
Occam's Razor in sensorimotor learning
Genewein, T, Braun, D
Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014 (article)
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants' choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants' drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.
ei Genewein, T, Braun, D Occam's Razor in sensorimotor learning Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014 (article)
Generalized Thompson sampling for sequential decision-making and causal inference
Ortega, PA, Braun, DA
Complex Adaptive Systems Modeling, 2(2):1-23, March 2014 (article)
Purpose Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling. Methods Although mostly applied to bandit problems, Thompson sampling can also be used to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution over actions can then be constructed by a Bayesian superposition of the policies weighted by their posterior probability of being optimal. Results Here we discuss two important features of this approach. First, we show in how far such generalized Thompson sampling can be regarded as an optimal strategy under limited information processing capabilities that constrain the sampling complexity of the decision-making process. Second, we show how such Thompson sampling can be extended to solve causal inference problems when interacting with an environment in a sequential fashion. Conclusion In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.
ei Ortega, PA, Braun, DA Generalized Thompson sampling for sequential decision-making and causal inference Complex Adaptive Systems Modeling, 2(2):1-23, March 2014 (article)
Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences
Peng, Z, Genewein, T, Braun, DA
Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects' self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.
ei Peng, Z, Genewein, T, Braun, DA Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)
Modelling contrast discrimination data suggest both the pedestal effect and stochastic resonance to be caused by the same mechanism
Goris, R., Wagemans, J., Wichmann, F.
Journal of Vision, 8(15):1-21, November 2008 (article)
Computational models of spatial vision typically make use of a (rectified) linear filter, a nonlinearity and dominant late noise to account for human contrast discrimination data. Linear–nonlinear cascade models predict an improvement in observers' contrast detection performance when low, subthreshold levels of external noise are added (i.e., stochastic resonance). Here, we address the issue whether a single contrast gain-control model of early spatial vision can account for both the pedestal effect, i.e., the improved detectability of a grating in the presence of a low-contrast masking grating, and stochastic resonance. We measured contrast discrimination performance without noise and in both weak and moderate levels of noise. Making use of a full quantitative description of our data with few parameters combined with comprehensive model selection assessments, we show the pedestal effect to be more reduced in the presence of weak noise than in moderate noise. This reduction rules out independent, additive sources of performance improvement and, together with a simulation study, supports the parsimonious explanation that a single mechanism underlies the pedestal effect and stochastic resonance in contrast perception.
ei Goris, R., Wagemans, J., Wichmann, F. Modelling contrast discrimination data suggest both the pedestal effect and stochastic resonance to be caused by the same mechanism Journal of Vision, 8(15):1-21, November 2008 (article)
Frequent Subgraph Retrieval in Geometric Graph Databases
Nowozin, S., Tsuda, K.
(180), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)
Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In such applications, scientists are not interested in the statistics of the whole database. Instead they need information about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph which are frequent geometric epsilon-subgraphs under the entire class of rigid geometric transformations in a database. By using geometric epsilon-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per pattern is larger than for non-geometric graph mining, the total time is within a reasonable level even for small minimum support.
ei Nowozin, S., Tsuda, K. Frequent Subgraph Retrieval in Geometric Graph Databases (180), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)
gBoost: A Mathematical Programming Approach to Graph Classification and Regression
Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., Tsuda, K.
Machine Learning, 75(1):69-89, November 2008 (article)
Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm.
ei Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., Tsuda, K. gBoost: A Mathematical Programming Approach to Graph Classification and Regression Machine Learning, 75(1):69-89, November 2008 (article)