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 331–340 of 1262 papers

TitleStatusHype
Active Search for Sparse Signals with Region Sensing—0
Boltzmann Exploration Done Right—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
Best-of-Both-Worlds Linear Contextual Bandits—0
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Benchmark Results

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