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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 81–90 of 1262 papers

TitleStatusHype
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
Variance-Dependent Regret Lower Bounds for Contextual Bandits—0
Locally Private Nonparametric Contextual Multi-armed BanditsCode0
Multiplayer Information Asymmetric Contextual Bandits—0
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference—0
Cost-Aware Optimal Pairwise Pure Exploration—0
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure—0
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits—0
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
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Benchmark Results

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