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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 601–650 of 655 papers

TitleStatusHype
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Asymptotic Convergence of Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Asynchronous Multi Agent Active Search—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Automatic Ensemble Learning for Online Influence Maximization—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Bag of Policies for Distributional Deep Exploration—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Bandit Learning for Diversified Interactive Recommendation—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Bandits Under The Influence (Extended Version)—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Bayesian learning of the optimal action-value function in a Markov decision process—0
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
Bayesian Quantile and Expectile Optimisation—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Belief Flows of Robust Online Learning—0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Blind Exploration and Exploitation of Stochastic Experts—0
Bootstrapped Thompson Sampling and Deep Exploration—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
Calibrated Fairness in Bandits—0
Causal Bandits without prior knowledge using separating sets—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
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