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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

Showing 431440 of 655 papers

TitleStatusHype
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints0
Learning to Rank in the Position Based Model with Bandit Feedback0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization0
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D0
Thompson Sampling for Linearly Constrained BanditsCode0
Optimal No-regret Learning in Repeated First-price Auctions0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits0
Online Residential Demand Response via Contextual Multi-Armed Bandits0
Odds-Ratio Thompson Sampling to Control for Time-Varying EffectCode0
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