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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
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
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
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
Deep Active Ensemble Sampling For Image Classification—0
The Typical Behavior of Bandit Algorithms—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
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Langevin Monte Carlo for Contextual BanditsCode1
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
On Provably Robust Meta-Bayesian OptimizationCode0
Top Two Algorithms Revisited—0
Regret Bounds for Information-Directed Reinforcement Learning—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Incentivizing Combinatorial Bandit Exploration—0
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