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 951–975 of 1262 papers

TitleStatusHype
Unreliable Multi-Armed Bandits: A Novel Approach to Recommendation Systems—0
Triply Robust Off-Policy Evaluation—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Multi-Armed Bandits with Correlated ArmsCode0
Persistency of Excitation for Robustness of Neural NetworksCode0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling via Local UncertaintyCode0
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Smoothness-Adaptive Contextual BanditsCode0
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access—0
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions—0
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision ProcessesCode0
Adaptive Exploration in Linear Contextual Bandit—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Regret Bounds for Batched Bandits—0
Privacy-Preserving Multi-Party Contextual Bandits—0
Social Learning in Multi Agent Multi Armed Bandits—0
Decision Automation for Electric Power Network Recovery—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
Show:102550
← PrevPage 39 of 51Next →

Benchmark Results

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