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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 226–250 of 655 papers

TitleStatusHype
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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