Optimizing importance weighting in the presence of sub-population shifts

Floris Holstege, Bram Wouters, Noud Van Giersbergen, Cees Diks

Keywords: Importance weighting, Distribution shift, Group Robustness, Bi-level optimization

TL;DR: We show that existing importance weighting methods for sub-population shift can be improved using a bi-level optimization procedure over the weights

Abstract: A distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different weights to data points during training. We argue that existing heuristics for determining the weights are suboptimal, as they neglect the increase of the variance of the estimated model due to the limited sample size of the training data. We interpret the optimal weights in terms of a bias-variance trade-off, and propose a bi-level optimization procedure in which the weights and model parameters are optimized simultaneously. We apply this framework to existing importance weighting techniques for last-layer retraining of deep neural networks in the presence of sub-population shifts and show empirically that optimizing weights significantly improves generalization performance.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 4672