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 701–725 of 1262 papers

TitleStatusHype
Reinforcement Learning for Physical Layer CommunicationsCode0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Smooth Sequential Optimisation with Delayed Feedback—0
Banker Online Mirror Descent—0
Guaranteed Fixed-Confidence Best Arm Identification in Multi-Armed Bandits: Simple Sequential Elimination Algorithms—0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
Scale Free Adversarial Multi Armed Bandits—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
Generalized Linear Bandits with Local Differential PrivacyCode1
On Learning to Rank Long Sequences with Contextual Bandits—0
Multi-facet Contextual Bandits: A Neural Network PerspectiveCode0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks—0
Fair Exploration via Axiomatic Bargaining—0
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Parallelizing Contextual Bandits—0
Diffusion Approximations for Thompson Sampling—0
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Show:102550
← PrevPage 29 of 51Next →

Benchmark Results

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