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 51–75 of 1262 papers

TitleStatusHype
Contextual Bandits with Stochastic ExpertsCode0
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox OptimizationCode0
Corralling a Band of Bandit AlgorithmsCode0
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
Constrained regret minimization for multi-criterion multi-armed banditsCode0
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action SpacesCode0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Doubly-Robust Lasso BanditCode0
Efficient Algorithms for Extreme BanditsCode0
Adaptive Linear Estimating EquationsCode0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Scalable Exploration via Ensemble++Code0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
Conditionally Risk-Averse Contextual BanditsCode0
Adaptive Experimentation with Delayed Binary FeedbackCode0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Active Feature Selection for the Mutual Information CriterionCode0
Causally Abstracted Multi-armed BanditsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Show:102550
← PrevPage 3 of 51Next →

Benchmark Results

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