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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 751–800 of 1262 papers

TitleStatusHype
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Encrypted Linear Contextual Bandit—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems—0
Nearest Neighbor Search Under Uncertainty—0
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes—0
Fairness of Exposure in Stochastic Bandits—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Local Clustering in Contextual Multi-Armed Bandits—0
Federated Multi-armed Bandits with PersonalizationCode0
Online Multi-Armed Bandits with Adaptive Inference—0
Combinatorial Bandits under Strategic ManipulationsCode0
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Meta-Thompson Sampling—0
Player Modeling via Multi-Armed Bandits—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Multi-Agent Multi-Armed Bandits with Limited Communication—0
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits—0
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap—0
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
Bandits for Learning to Explain from Explanations—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Transfer Learning in Bandits with Latent Continuity—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits—0
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—0
Be Greedy in Multi-Armed Bandits—0
Online Learning under Adversarial Corruptions—0
Online Limited Memory Neural-Linear Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks—0
Lifelong Learning in Multi-Armed Bandits—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Relational Boosted BanditsCode0
A One-Size-Fits-All Solution to Conservative Bandit Problems—0
Active Feature Selection for the Mutual Information CriterionCode0
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations—0
Streaming Algorithms for Stochastic Multi-armed Bandits—0
Efficient Automatic CASH via Rising Bandits—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Neural Contextual Bandits with Deep Representation and Shallow Exploration—0
Distributed Thompson Sampling—0
Batched Coarse Ranking in Multi-Armed Bandits—0
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
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Benchmark Results

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