Robust Learning in Heterogeneous Contexts release_fzdyzffrbfabdidqb3cxy5kc3u

by Muhammad Osama, Dave Zachariah, Petre Stoica

Released as a article .

2022  

Abstract

We consider the problem of learning from training data obtained in different contexts, where the underlying context distribution is unknown and is estimated empirically. We develop a robust method that takes into account the uncertainty of the context distribution. Unlike the conventional and overly conservative minimax approach, we focus on excess risks and construct distribution sets with statistical coverage to achieve an appropriate trade-off between performance and robustness. The proposed method is computationally scalable and shown to interpolate between empirical risk minimization and minimax regret objectives. Using both real and synthetic data, we demonstrate its ability to provide robustness in worst-case scenarios without harming performance in the nominal scenario.
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Date   2022-02-17
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arXiv  2105.08532v3
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