no code implementations • 7 Dec 2023 • Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward $g(\gamma, v)$ depends on the proposed threshold $\gamma$ and latent value $v$ and it can be $only$ achieved if the threshold is lower than or equal to the unknown latent value.
no code implementations • 30 Jun 2023 • Robert Kleinberg, Renato Paes Leme, Jon Schneider, Yifeng Teng
We show that sublinear U-calibration error is a necessary and sufficient condition for all agents to achieve sublinear regret guarantees.
no code implementations • 8 Jun 2021 • Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation.
