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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

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Showing 976–1000 of 1262 papers

TitleStatusHype
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Towards Soft Fairness in Restless Multi-Armed Bandits—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
Tracking Most Significant Shifts in Nonparametric Contextual Bandits—0
Tractable contextual bandits beyond realizability—0
Transfer in Sequential Multi-armed Bandits via Reward Samples—0
Transfer Learning for Contextual Multi-armed Bandits—0
Transfer Learning in Bandits with Latent Continuity—0
Tree Ensembles for Contextual Bandits—0
Trend Detection based Regret Minimization for Bandit Problems—0
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System—0
Triply Robust Off-Policy Evaluation—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
UCB algorithms for multi-armed bandits: Precise regret and adaptive inference—0
Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits—0
Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits—0
Unifying Clustered and Non-stationary Bandits—0
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs—0
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Unreliable Multi-Armed Bandits: A Novel Approach to Recommendation Systems—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
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Benchmark Results

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