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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 401–450 of 655 papers

TitleStatusHype
Position-Based Multiple-Play Bandits with Thompson Sampling—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Partially Observable Online Change Detection via Smooth-Sparse Decomposition—0
Bandits Under The Influence (Extended Version)—0
Causal Bandits without prior knowledge using separating sets—0
Thompson Sampling for Unsupervised Sequential Selection—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Contextual Bandits for Advertising Budget Allocation—0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
Reinforcement Learning with Trajectory Feedback—0
Lenient Regret for Multi-Armed Bandits—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Greedy Bandits with Sampled Context—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Variable Selection via Thompson Sampling—0
Policy Gradient Optimization of Thompson Sampling Policies—0
Asynchronous Multi Agent Active Search—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
Latent Bandits Revisited—0
Hypermodels for Exploration—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
On Frequentist Regret of Linear Thompson Sampling—0
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits—0
Scalable Thompson Sampling using Sparse Gaussian Process Models—0
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D—0
Thompson Sampling for Linearly Constrained BanditsCode0
Optimal No-regret Learning in Repeated First-price Auctions—0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
Odds-Ratio Thompson Sampling to Control for Time-Varying EffectCode0
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles—0
MOTS: Minimax Optimal Thompson Sampling—0
Efficient exploration of zero-sum stochastic games—0
On Thompson Sampling with Langevin Algorithms—0
Residual Bootstrap Exploration for Bandit Algorithms—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Thompson Sampling Algorithms for Mean-Variance BanditsCode0
Bayesian Quantile and Expectile Optimisation—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
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