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
Surprisingly, without task-specific fine-tuning or calibration to human bids, the three non-reasoning large models robustly preserve key orderings of auction formats by deviation from theory.
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
The experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs.
S. Ravindranath, Yan-Chen Jiang, David C. Parkes· Neural Information Processin...· 17 citations
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