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 1051–1075 of 1262 papers

TitleStatusHype
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
Adaptive Exploration in Linear Contextual Bandit—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
Adaptively Learning to Select-Rank in Online Platforms—0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
ADARES: Adaptive Resource Management for Virtual Machines—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
Bandits with Knapsacks beyond the Worst-Case—0
Adversarial Attacks on Adversarial Bandits—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Adversarial Attacks on Linear Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
Adversarial Contextual Bandits Go Kernelized—0
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations—0
α-Fair Contextual Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
A Gang of Bandits—0
A General Framework for Bandit Problems Beyond Cumulative Objectives—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Show:102550
← PrevPage 43 of 51Next →

Benchmark Results

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