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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

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Showing 551–575 of 655 papers

TitleStatusHype
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Thompson Sampling for Dynamic Pricing—0
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
On Adaptive Estimation for Dynamic Bernoulli Bandits—0
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds—0
Efficient exploration with Double Uncertain Value Networks—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Estimating prediction error for complex samples—0
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
The Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Minimal Exploration in Structured Stochastic Bandits—0
Sequential Matrix Completion—0
A study of Thompson Sampling with Parameter h—0
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
Bayesian bandits: balancing the exploration-exploitation tradeoff via double samplingCode0
Variational inference for the multi-armed contextual banditCode0
Learning to Price with Reference Effects—0
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors—0
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems—0
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