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Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 951–1000 of 1262 papers

TitleStatusHype
Unreliable Multi-Armed Bandits: A Novel Approach to Recommendation Systems—0
Triply Robust Off-Policy Evaluation—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Multi-Armed Bandits with Correlated ArmsCode0
Persistency of Excitation for Robustness of Neural NetworksCode0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling via Local UncertaintyCode0
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Smoothness-Adaptive Contextual BanditsCode0
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access—0
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions—0
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision ProcessesCode0
Adaptive Exploration in Linear Contextual Bandit—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Regret Bounds for Batched Bandits—0
Privacy-Preserving Multi-Party Contextual Bandits—0
Social Learning in Multi Agent Multi Armed Bandits—0
Decision Automation for Electric Power Network Recovery—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
Learning Effective Exploration Strategies For Contextual Bandits—0
Practical Calculation of Gittins Indices for Multi-armed BanditsCode0
AutoML for Contextual Bandits—0
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret RegimesCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits—0
Nonparametric Contextual Bandits in an Unknown Metric Space—0
Doubly-Robust Lasso BanditCode0
Scaling Multi-Armed Bandit Algorithms—0
Doubly robust off-policy evaluation with shrinkage—0
Parameterized Exploration—0
Productization Challenges of Contextual Multi-Armed Bandits—0
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits—0
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates—0
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules—0
Online Allocation and Pricing: Constant Regret via Bellman Inequalities—0
Competing Bandits in Matching Markets—0
Bootstrapping Upper Confidence Bound—0
Beam Learning -- Using Machine Learning for Finding Beam Directions—0
Stochastic Neural Network with Kronecker Flow—0
Balanced off-policy evaluation in general action spaces—0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
Empirical Likelihood for Contextual BanditsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified