Learning robust models that adapt to shifting data distributions
Learning the Robustness Mechanism with Bilevel Optimization
Machine Learning
Summary
Machine learning models often struggle when the data they see changes in unexpected ways. This paper introduces a way to teach models to be more robust by automatically learning how to handle these changes using a special optimization process. Instead of guessing how to make the model tough against tricky data, the method learns the best settings from extra data. The authors show that this approach works well both in theory and practice, especially when different groups of data change in various ways at the same time.
What this means in practice
- •For machine learning engineers: Train models that better maintain accuracy when data distributions change unexpectedly in real-world deployments.
- •For data scientists in finance: Improve prediction reliability when financial data shifts within or across groups, reducing risk in automated decisions.
Authors
Yiyang Shen, Qihang Lin, Weiran Wang
Abstract
We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of extensively tuned. Using bilevel optimization with both upper and lower level minimax problems, we create two instances of our framework to tackle setups with and without group labels in the training set. Theoretically, we provide sample complexity analysis for our robustness mechanism learning paradigm, showing that it achieves generalization guarantees comparable to exhaustive grid search while being more computationally efficient. Empirically, we evaluate our framework under a challenging setup when both intra-group and inter-group test distribution shifts occur at the same time, thereby demonstrating the efficacy and scalability of our method.