2019/01/31 by Koji Tabata, Atsuyoshi Nakamura, Tabata, Koji +5 · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1901.11200
arxiv created 2019/01/31 · openalex publication_date 2019/01/31 · arxiv updated 2019/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative (non-positive) arms' roles; finding one positive arm is enough to judge existence while all the arms must be discriminated as negative to judge non-existence. We propose an algorithms with arm selection policy (policy to determine the next arm to draw) and stopping condition (condition to stop drawing arms) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.