SOTAVerified

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 751–775 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
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Benchmark Results

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