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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 226–250 of 655 papers

TitleStatusHype
Sample Efficient Learning of Factored Embeddings of Tensor Fields—0
Causal Bandits for Linear Structural Equation ModelsCode0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Using Adaptive Experiments to Rapidly Help Students—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Ranking In Generalized Linear BanditsCode0
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Langevin Monte Carlo for Contextual BanditsCode1
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
On Provably Robust Meta-Bayesian OptimizationCode0
Top Two Algorithms Revisited—0
Regret Bounds for Information-Directed Reinforcement Learning—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Incentivizing Combinatorial Bandit Exploration—0
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