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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 1262 papers

TitleStatusHype
Bandit Regret Scaling with the Effective Loss Range—0
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Bandits for Learning to Explain from Explanations—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
Bandit Social Learning: Exploration under Myopic Behavior—0
Bandits Warm-up Cold Recommender Systems—0
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms—0
Bandits with Knapsacks beyond the Worst Case—0
Bandits with Partially Observable Confounded Data—0
Bandits with Temporal Stochastic Constraints—0
Banker Online Mirror Descent—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Batched Bandits with Crowd Externalities—0
Batched Coarse Ranking in Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Regret Bounds for Batched Bandits—0
Batched Nonparametric Bandits via k-Nearest Neighbor UCB—0
Breaking the (1/Δ_2) Barrier: Better Batched Best Arm Identification with Adaptive Grids—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Towards Bayesian Data Selection—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
Beam Learning -- Using Machine Learning for Finding Beam Directions—0
Be Greedy in Multi-Armed Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Best Arm Identification in Linked Bandits—0
A Gang of Bandits—0
Best Arm Identification in Restless Markov Multi-Armed Bandits—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Best Arm Identification under Additive Transfer Bandits—0
An Empirical Evaluation of Thompson Sampling—0
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits—0
Best-of-Both-Worlds Linear Contextual Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
Boltzmann Exploration Done Right—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Balanced Linear Contextual Bandits—0
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Benchmark Results

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