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Title: Distributionally Robust Group Backwards Compatibility
Machine learning models are updated as new data is acquired or new architectures are developed. These updates usually increase model performance, but may introduce backward compatibility errors, where individual users or groups of users see their performance on the updated model adversely affected. This problem can also be present when training datasets do not accurately reflect overall population demographics, with some groups having overall lower participation in the data collection process, posing a significant fairness concern. We analyze how ideas from distributional robustness and minimax fairness can aid backward compatibility in this scenario, and propose two methods to directly address this issue. Our theoretical analysis is backed by experimental results on CIFAR-10, CelebA, and Waterbirds, three standard image classification datasets.  more » « less
Award ID(s):
2120018 2031849 1712867
NSF-PAR ID:
10347290
Author(s) / Creator(s):
Date Published:
Journal Name:
NeurIPS 2021 Workshop DistShift
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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