ModelDiff: A Framework for Comparing Learning Algorithms

• URL: http://arxiv.org/abs/2211.12491v1
• Date: Tue, 22 Nov 2022 18:56:52 GMT
• Title: ModelDiff: A Framework for Comparing Learning Algorithms
• Authors: Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry
• Abstract summary: We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We present ModelDiff, a method that leverages the datamodels framework to compare learning algorithms based on how they use their training data.
• Score: 86.19580801269036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present ModelDiff, a method that leverages the datamodels framework (Ilyas et al., 2022) to compare learning algorithms based on how they use their training data. We demonstrate ModelDiff through three case studies, comparing models trained with/without data augmentation, with/without pre-training, and with different SGD hyperparameters. Our code is available at https://github.com/MadryLab/modeldiff .

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