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
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits—0
Are sample means in multi-armed bandits positively or negatively biased?—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information—0
The Externalities of Exploration and How Data Diversity Helps Exploitation—0
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates—0
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication—0
The Pareto Frontier of model selection for general Contextual Bandits—0
The Price of Differential Privacy For Online Learning—0
Thompson Sampling for Budgeted Multi-armed Bandits—0
Thompson Sampling Algorithms for Cascading Bandits—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson sampling for improved exploration in GFlowNets—0
Thompson Sampling for Unsupervised Sequential Selection—0
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits—0
Tight Lower Bounds for Combinatorial Multi-Armed Bandits—0
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits—0
Top-K Ranking Deep Contextual Bandits for Information Selection Systems—0
To update or not to update? Delayed Nonparametric Bandits with Randomized Allocation—0
Towards Distribution-Free Multi-Armed Bandits with Combinatorial Strategies—0
Towards Domain Adaptive Neural Contextual Bandits—0
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Benchmark Results

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