Large Deviation Principle

Protected: Extension of the Bandit Problem – Time-Varying Bandit Problem and Comparative Bandit

Time-varying bandit problems and comparative bandits as extensions of bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks RMED measures, Condorcet winner, empirical divergence, large deviation principle, Borda winner, Coplan Winner, Thompson Extraction, Weak Riglet, Total Order Assumption, Sleeping Bandit, Ruined Bandit, Non-Dormant Bandit, Discounted UCB Measures, UCB Measures, Hostile Bandit, Exp3 Measures, LinUCB, Contextual Bandit

2023.05.25

アルゴリズム:Algorithmsグラフ理論スパースモデリングバンディッド問題幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Measures for Stochastic Banded Problems Likelihood-based measures (UCB and MED measures)

Measures for Stochastic Banded Problems Likelihood-based UCB and MED measures (Indexed Maximum Empirical Divergence policy, KL-UCB measures, DMED measures, Riglet upper bound, Bernoulli distribution, Large Deviation Principle, Deterministic Minimum Empirical Divergence policy, Newton's method, KL divergence, Binsker's inequality, Heffding's inequality, Chernoff-Heffding inequality, Upper Confidence Bound)

2022.12.09

アルゴリズム:Algorithmsバンディッド問題幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Fundamentals of Stochastic Bandid Problems

Basics of stochastic bandid problems utilized in digital transformation, artificial intelligence, and machine learning tasks (large deviation principle and examples in Bernoulli distribution, Chernoff-Heffding inequality, Sanov's theorem, Heffding inequality, Kullback-Leibler divergence, probability mass function, hem probability, probability approximation by central limit theorem).

2022.11.11

バンディッド問題強化学習微分積分:Calculus機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra