Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage
We study offline learning in two-player zero-sum contextual games with public and private information, motivated by strategic settings such as auctions and negotiations with private valuations. We introduce unilateral prescriptive concentrability and show that asymmetric information can change offline coverage through its effect on equilibrium behavior. For finite state-action spaces, we develop a pessimistic algorithm with an $\tilde{O}(1/\sqrt{n})$ exploitability rate, matching the standard sample-size dependence for fully observed minimax games. We further develop a pessimistic policy mirror descent framework, PPA-PMD, for general function approximation and obtain a unified $\tilde{O}(...
| Authors | Shuze Daniel Liu, Claire Chen, Jiuqi Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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