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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 651–700 of 1262 papers

TitleStatusHype
Offline Contextual Bandits for Wireless Network Optimization—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit—0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
(Almost) Free Incentivized Exploration from Decentralized Learning AgentsCode0
Federated Linear Contextual Bandits—0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
The Pareto Frontier of model selection for general Contextual Bandits—0
Linear Contextual Bandits with Adversarial Corruptions—0
Towards the D-Optimal Online Experiment Design for Recommender SelectionCode0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Dynamic pricing and assortment under a contextual MNL demand—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Query-Reward Tradeoffs in Multi-Armed Bandits—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Batched Thompson Sampling—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Causal Contextual Bandits with Targeted Interventions—0
Expected Improvement-based Contextual Bandits—0
Batched Bandits with Crowd Externalities—0
Risk averse non-stationary multi-armed bandits—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits—0
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health—0
Estimation of Warfarin Dosage with Reinforcement LearningCode0
Exploiting Heterogeneity in Robust Federated Best-Arm Identification—0
Improved Algorithms for Misspecified Linear Markov Decision Processes—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
Online Learning for Cooperative Multi-Player Multi-Armed Bandits—0
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations—0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Maximizing and Satisficing in Multi-armed Bandits with Graph InformationCode0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Finite-time Analysis of Globally Nonstationary Multi-Armed BanditsCode0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
An Analysis of Reinforcement Learning for Malaria Control—0
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Benchmark Results

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