2012/06/27 by Gábor Bartók, Bartok, Gabor, Navid Zolghadr +3 · 3 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computer Science and Game Theory (cs.GT) #Data Stream Mining Techniques #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1206.6487
openalex publication_date 2012/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new anytime algorithm that achieves near-optimal regret for any instance of finite stochastic partial monitoring. In particular, the new algorithm achieves the minimax regret, within logarithmic factors, for both "easy" and "hard" problems. For easy problems, it additionally achieves logarithmic individual regret. Most importantly, the algorithm is adaptive in the sense that if the opponent strategy is in an "easy region" of the strategy space then the regret grows as if the problem was easy. As an implication, we show that under some reasonable additional assumptions, the algorithm enjoys an O(√(T)) regret in Dynamic Pricing, proven to be hard by Bartok et al. (2011).