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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–325 of 1262 papers

TitleStatusHype
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
ADARES: Adaptive Resource Management for Virtual Machines—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Budgeted Recommendation with Delayed Feedback—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
Budgeted Combinatorial Multi-Armed Bandits—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
An Instrumental Value for Data Production and its Application to Data Pricing—0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
Breaking the (1/Δ_2) Barrier: Better Batched Best Arm Identification with Adaptive Grids—0
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit—0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
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Benchmark Results

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