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 876–900 of 1262 papers

TitleStatusHype
Second Order Bounds for Contextual Bandits with Function Approximation—0
Selecting the best system and multi-armed bandits—0
Selective Harvesting over Networks—0
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes—0
Selectively Contextual Bandits—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Selfish Robustness and Equilibria in Multi-Player Bandits—0
Self-Supervised Contextual Bandits in Computer Vision—0
Self-Tuning Bandits over Unknown Covariate-Shifts—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Semi-Parametric Batched Global Multi-Armed Bandits with Covariates—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
Sequential Batch Learning in Finite-Action Linear Contextual Bandits—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems—0
Sequential Design for Ranking Response Surfaces—0
Sequential Monte Carlo Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
Shuffle Private Linear Contextual Bandits—0
Simple Regret Minimization for Contextual Bandits—0
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition—0
Skyline Identification in Multi-Armed Bandits—0
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Benchmark Results

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