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 951–1000 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
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
Show:102550
← PrevPage 20 of 26Next →

Benchmark Results

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