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ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah · Sung Min (Sam) Park · Andrew Ilyas · Aleksander Madry

Wed Jul 26 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #407

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.

Author Information

Harshay Shah (Massachusetts Institute of Technology)
Sung Min (Sam) Park (MIT)
Andrew Ilyas (MIT)
Aleksander Madry (MIT)

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