Robust Transfer Learning with Side Information
Robust Markov Decision Processes (MDPs) address environmental shift through distributionally robust optimization (DRO) by finding an optimal worst-case policy within an uncertainty set of transition kernels. However, standard DRO approaches require enlarging the uncertainty set under large shifts, which leads to overly conservative and pessimistic policies. In this paper, we propose a framework for transfer under environment shift that derives a robust target-domain policy via estimate-centered uncertainty sets, constructed through constrained estimation that integrates limited target samples with side information about the source-target dynamics. The side information includes bounds on f...
| Authors | Akram S. Awad, Shihab Ahmed, Yue Wang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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