  In this work, we propose ModelPred, a framework that helps to understand the
impact of changes in training data on a trained model. This is critical for
building trust in various stages of a machine learning pipeline: from cleaning
poor-quality samples and tracking important ones to be collected during data
preparation, to calibrating uncertainty of model prediction, to interpreting
why certain behaviors of a model emerge during deployment. Specifically,
ModelPred learns a parameterized function that takes a dataset $S$ as the input
and predicts the model obtained by training on $S$. Our work differs from the
recent work of Datamodels [1] as we aim for predicting the trained model
parameters directly instead of the trained model behaviors. We demonstrate that
a neural network-based set function class is capable of learning the complex
relationships between the training data and model parameters. We introduce
novel global and local regularization techniques to prevent overfitting and we
rigorously characterize the expressive power of neural networks (NN) in
approximating the end-to-end training process. Through extensive empirical
investigations, we show that ModelPred enables a variety of applications that
boost the interpretability and accountability of machine learning (ML), such as
data valuation, data selection, memorization quantification, and model
calibration.
