2008/06/30 by Yoav Freund, Freund, Yoav, Daniel Hsu +1 · 2 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning and Algorithms #cs.AI #cs.GT
paper · pdf · doi:10.48550/arxiv.0806.4802
arxiv created 2008/06/30 · openalex publication_date 2008/06/30 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new online learning algorithm for cumulative discounted gain. This learning algorithm does not use exponential weights on the experts. Instead, it uses a weighting scheme that depends on the regret of the master algorithm relative to the experts. In particular, experts whose discounted cumulative gain is smaller (worse) than that of the master algorithm receive zero weight. We also sketch how a regret-based algorithm can be used as an alternative to Bayesian averaging in the context of inferring latent random variables.