Convergence of Optimistic Learning in Games and the Role of Forgetfulness
Online learning algorithms solve games by repeatedly updating players’ strategies. A natural hope is that the latest strategy improves at a predictable rate. This paper shows that this intuition can fail for optimistic follow-the-regularized-lea...
Yang Cai, Gabriele Farina, J. Grand-Clément et al.· Operational Research· 0 citations
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a return-on-spend (ROS) constraint--rather than manual per-query bids. A central question in algorithmic mechanism design is characterizing the wo...
Yang Cai, Vineet Gupta, Yan-Chen Jiang et al.· 1 citation
We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a single shot, single-shot generation is often insufficient for open problems that require exploring multiple competing conjectures, overcoming sub...
Yang Cai, Vineet Gupta, Yan-Chen Jiang et al.· 0 citations
An algorithm is given, requiring no knowledge of the horizon, whose cumulative regret satisfies R_t, whose cumulative regret satisfies 1 + O(\sqrt{\ln \ln n / \ln n})\bigr)\sqrt{t \ln n / 2}$ simultaneously for every $t \ge 1$.
Yang Cai, Vineet Gupta, Yan-Chen Jiang et al.· 1 citation
We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bound of this order was obtained recently by Dewasurendra, settling a question of Gollapudi et al.\ and of Oki and Sakaue, but by an improper rule that enumerates covers a...
Yang Cai, Anupam Gupta, Vineet Gupta et al.· 2 citations· ⚡1
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