Distributionally Robust Learning in Heterogeneous Contexts.
Year: 2021
Type: preprint
Abstract: We consider the problem of learning from training data obtained in different contexts, where the test data is subject to distributional shifts. We develop a distributionally robust method that focuses on excess risks and achieves a more appropriate trade-off between performance and robustness than the conventional and overly conservative minimax approach. The proposed method is computationally feasible and provides statistical guarantees. We demonstrate its performance using both real and synthetic data.
Source: arXiv (Cornell University)
Authors: Muhammad Osama, Dave Zachariah, Petre Stoica
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3160606002