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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 361–370 of 1262 papers

TitleStatusHype
A Convex Framework for Confounding Robust InferenceCode0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing—0
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Pure Exploration under Mediators' Feedback—0
Stochastic Graph Bandit Learning with Side-Observations—0
Learning How to Price Charging in Electric Ride-Hailing Markets—0
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Benchmark Results

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