2026-09-19T04:29:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1636752025-11-18T06:27:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Mądry, Aleksander
Shah, Harshay
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-11-17T19:06:38Z
2025-11-17T19:06:38Z
2025-05
2025-08-14T19:33:18.790Z
https://hdl.handle.net/1721.1/163675
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.
S.M.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
ModelDiff: A Framework for Comparing Learning Algorithms
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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<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.</Abstract>
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