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A Unified Framework for Comparing Learning Algorithms
Harshay Shah · Sung Min Park · Andrew Ilyas · Aleksander Madry
Event URL: https://openreview.net/forum?id=74UfM1WeBx »

Understanding model biases is crucial to understanding how models will perform out-of-distribution (OOD). These biases often stem from particular design choices (e.g., architecture or data augmentation). We propose a framework for (learning) algorithm comparisons, wherein the goal is to find similarities and differences between models trained with two different learning algorithms. We begin by formalizing the goal of algorithm comparison as finding distinguishing feature transformations, input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present a two-stage method for algorithm comparisons based on comparing how models use the training data, leveraging the recently proposed datamodel representations [IPE+22]. We demonstrate our framework through a case study comparing classifiers trained on the Waterbirds [SKH+20] dataset with/without ImageNet pre-training.

Author Information

Harshay Shah (MIT)
Sung Min Park (MIT)
Andrew Ilyas (MIT)
Aleksander Madry (MIT)

Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.

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