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 126–150 of 1262 papers

TitleStatusHype
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Bandits Warm-up Cold Recommender Systems—0
Bandits with Temporal Stochastic Constraints—0
Algorithms for multi-armed bandit problems—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Functional multi-armed bandit and the best function identification problems—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
Bandit Algorithms for Prophet Inequality and Pandora's Box—0
Ballooning Multi-Armed Bandits—0
A General Framework for Bandit Problems Beyond Cumulative Objectives—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
A Gang of Bandits—0
Show:102550
← PrevPage 6 of 51Next →

Benchmark Results

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