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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 176–200 of 1262 papers

TitleStatusHype
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