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 551–575 of 1262 papers

TitleStatusHype
Information-Directed Selection for Top-Two AlgorithmsCode0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Contextual Information-Directed Sampling—0
Pessimism for Offline Linear Contextual Bandits using _p Confidence Sets—0
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge EnvironmentsCode1
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes—0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Selectively Contextual Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent SurfacesCode1
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Rate-Constrained Remote Contextual Bandits—0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations—0
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System—0
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk—0
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Benchmark Results

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