Optimizing importance weighting in the presence of sub-population shifts

• URL: http://arxiv.org/abs/2410.14315v1
• Date: Fri, 18 Oct 2024 09:21:10 GMT
• Title: Optimizing importance weighting in the presence of sub-population shifts
• Authors: Floris Holstege, Bram Wouters, Noud van Giersbergen, Cees Diks,
• Abstract summary: A distribution shift between the training and test data can severely harm performance of machine learning models. We argue that existing weightings for determining the weights are suboptimal, as they neglect the increase of the variance of the estimated model due to the finite sample size of the training data. We propose a bi-level optimization procedure in which the weights and model parameters are optimized simultaneously.
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• 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 finite 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 optimization 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.

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