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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 351–400 of 655 papers

TitleStatusHype
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment—0
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling—0
Meta-Thompson Sampling—0
On the Suboptimality of Thompson Sampling in High DimensionsCode0
State-Aware Variational Thompson Sampling for Deep Q-NetworksCode0
Doubly robust Thompson sampling for linear payoffs—0
Weak Signal Asymptotics for Sequentially Randomized Experiments—0
An empirical evaluation of active inference in multi-armed banditsCode1
Scalable Optimization for Wind Farm Control using Coordination GraphsCode0
TSEC: a framework for online experimentation under experimental constraints—0
Deciding What to Learn: A Rate-Distortion Approach—0
Etat de l'art sur l'application des bandits multi-bras—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
Learning to Play Imperfect-Information Games by Imitating an Oracle PlannerCode0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
Mercer Features for Efficient Combinatorial Bayesian OptimizationCode1
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Optimal Thompson Sampling strategies for support-aware CVaR banditsCode1
Distributed Thompson Sampling—0
Non-Stationary Latent Bandits—0
On Efficiency in Hierarchical Reinforcement Learning—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
Accelerating Grasp Exploration by Leveraging Learned Priors—0
Thompson sampling for linear quadratic mean-field teams—0
Multi-Agent Active Search using Realistic Depth-Aware Noise ModelCode0
Asymptotic Convergence of Thompson Sampling—0
Adaptive Combinatorial Allocation—0
Multi-armed Bandits with Cost Subsidy—0
Greedy k-Center from Noisy Distance Samples—0
Screening for an Infectious Disease as a Problem in Stochastic Control—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Sub-sampling for Efficient Non-Parametric Bandit ExplorationCode0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Bayesian Algorithms for Decentralized Stochastic BanditsCode0
Federated Bayesian Optimization via Thompson SamplingCode1
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation—0
Double-Linear Thompson Sampling for Context-Attentive Bandits—0
Asynchronous ε-Greedy Bayesian OptimisationCode0
Online Learning and Distributed Control for Residential Demand Response—0
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization—0
Neural Thompson SamplingCode1
Stage-wise Conservative Linear Bandits—0
Neural Model-based Optimization with Right-Censored Observations—0
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
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