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 401–425 of 1262 papers

TitleStatusHype
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
Dynamic pricing and assortment under a contextual MNL demand—0
Dynamic Pricing with Limited Supply—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Early Stopping in Contextual Bandits and Inferences—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions—0
Efficient Action Poisoning Attacks on Linear Contextual Bandits—0
Designing an Interpretable Interface for Contextual Bandits—0
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems—0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits—0
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits—0
Efficient Automatic CASH via Rising Bandits—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
Efficient Contextual Bandits in Non-stationary Worlds—0
Delegating via Quitting Games—0
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
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Benchmark Results

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