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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
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Meta-Thompson Sampling—0
Multi-Agent Multi-Armed Bandits with Limited Communication—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits—0
Player Modeling via Multi-Armed Bandits—0
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap—0
Bandits for Learning to Explain from Explanations—0
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Transfer Learning in Bandits with Latent Continuity—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Federated Multi-Armed BanditsCode1
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
An empirical evaluation of active inference in multi-armed banditsCode1
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 Limited Memory Neural-Linear Bandits—0
Online Learning under Adversarial Corruptions—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
Distributed Thompson Sampling—0
Neural Contextual Bandits with Deep Representation and Shallow Exploration—0
Finding All -Good Arms in Stochastic Bandits—0
Batched Coarse Ranking in Multi-Armed Bandits—0
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit—0
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication—0
Fully Gap-Dependent Bounds for Multinomial Logit Bandit—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Improving Offline Contextual Bandits with Distributional Robustness—0
Metric-Free Individual Fairness with Cooperative Contextual Bandits—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
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Benchmark Results

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