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 1201–1225 of 1262 papers

TitleStatusHype
A Survey on Contextual Multi-armed BanditsCode0
Episodic Multi-armed Bandits—0
Linear Contextual Bandits with Knapsacks—0
Selecting the best system and multi-armed bandits—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
Scalable Discrete Sampling as a Multi-Armed Bandit Problem—0
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives—0
Regulating Greed Over Time in Multi-Armed BanditsCode0
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits—0
Thompson Sampling for Budgeted Multi-armed Bandits—0
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits—0
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond—0
Global Bandits—0
Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies—0
Doubly Robust Policy Evaluation and Optimization—0
Learning to Search Better Than Your Teacher—0
Combinatorial Pure Exploration of Multi-Armed Bandits—0
Learning Multiple Tasks in Parallel with a Shared Annotator—0
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback—0
On Minimax Optimal Offline Policy Evaluation—0
Bandits Warm-up Cold Recommender Systems—0
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms—0
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms—0
Reducing Dueling Bandits to Cardinal Bandits—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
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Benchmark Results

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