Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
- Università degli Studi di Milano（米兰大学）
- INRIA（法国国家信息与自动化研究所）
- École Normale Supérieure（巴黎高等师范学院）
- Università degli Studi dell’Insubria（因苏布里亚大学）
- Université Toulouse III - Paul Sabatier（图卢兹第三大学（保罗·萨巴蒂埃大学））
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedback model motivated by second-price auctions, we obtain algorithms for Lipschitz and semi-Lipschitz losses with regret bounds improving on the known bounds for standard bandit feedback. Our analysis combines novel results for contextual second-price auctions with a novel algorithmic approach based on chaining. When the context space is Euclidean, our chaining approach is efficient and delivers an even better regret bound.
