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 521–530 of 1262 papers

TitleStatusHype
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Langevin Monte Carlo for Contextual BanditsCode1
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms—0
On Private Online Convex Optimization: Optimal Algorithms in _p-Geometry and High Dimensional Contextual BanditsCode0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
Combinatorial Pure Exploration of Causal Bandits—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
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Benchmark Results

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