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 41–50 of 1262 papers

TitleStatusHype
Combinatorial Bandits under Strategic ManipulationsCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
Conditionally Risk-Averse Contextual BanditsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Causally Abstracted Multi-armed BanditsCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Bandit-Based Monte Carlo Optimization for Nearest NeighborsCode0
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
Show:102550
← PrevPage 5 of 127Next →

Benchmark Results

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