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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 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
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—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
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—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
Best-Arm Identification in Correlated Multi-Armed Bandits—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
Balanced off-policy evaluation in general action spaces—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