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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 201–250 of 655 papers

TitleStatusHype
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Trajectory-oriented optimization of stochastic epidemiological modelsCode0
An improved regret analysis for UCB-N and TS-N—0
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
When Combinatorial Thompson Sampling meets Approximation Regret—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Leveraging Demonstrations to Improve Online Learning: Quality Matters—0
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits—0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
Thompson Sampling with Diffusion Generative Prior—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Neural Bandits for Data Mining: Searching for Dangerous PolypharmacyCode0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning ApproachCode0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
The Typical Behavior of Bandit Algorithms—0
Deep Active Ensemble Sampling For Image Classification—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Thompson Sampling with Virtual Helping Agents—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
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
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