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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
Thompson Sampling for Gaussian Entropic Risk Bandits—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Blind Exploration and Exploitation of Stochastic Experts—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—0
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection—0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Online Multi-Armed Bandits with Adaptive Inference—0
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
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
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Distributed Thompson Sampling—0
On Efficiency in Hierarchical Reinforcement Learning—0
Non-Stationary Latent Bandits—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Accelerating Grasp Exploration by Leveraging Learned Priors—0
Multi-Agent Active Search using Realistic Depth-Aware Noise ModelCode0
Thompson sampling for linear quadratic mean-field teams—0
Asymptotic Convergence of Thompson Sampling—0
Adaptive Combinatorial Allocation—0
Greedy k-Center from Noisy Distance Samples—0
Multi-armed Bandits with Cost Subsidy—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
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
Stage-wise Conservative Linear Bandits—0
Neural Model-based Optimization with Right-Censored Observations—0
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