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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 1262 papers

TitleStatusHype
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
Parallel Best Arm Identification in Heterogeneous Environments—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Adversarial Attacks on Adversarial Bandits—0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards—0
Combinatorial Pure Exploration of Multi-Armed Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Combinatorial Semi-Bandits with Knapsacks—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Adversarial Bandits with Knapsacks—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
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Benchmark Results

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