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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1150 of 1262 papers

TitleStatusHype
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Delegating via Quitting Games—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
Best arm identification in multi-armed bandits with delayed feedback—0
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits—0
Semiparametric Contextual BanditsCode0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
Online learning over a finite action set with limited switching—0
Practical Contextual Bandits with Regression Oracles—0
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates—0
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
Contextual Bandits with Stochastic ExpertsCode0
Regional Multi-Armed Bandits—0
Online Learning with an Unknown Fairness Metric—0
Policy Gradients for Contextual Recommendations—0
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs—0
More Robust Doubly Robust Off-policy Evaluation—0
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits—0
Nonparametric Stochastic Contextual Bandits—0
Residual Loss Prediction: Reinforcement Learning With No Incremental FeedbackCode0
Contextual memory bandit for pro-active dialog engagement—0
Learning Structural Weight Uncertainty for Sequential Decision-MakingCode0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Stochastic Multi-armed Bandits in Constant Space—0
Gaussian Process bandits with adaptive discretization—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
Online Learning via the Differential Privacy Lens—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Estimation Considerations in Contextual Bandits—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
Skyline Identification in Multi-Armed Bandits—0
Small-loss bounds for online learning with partial information—0
Multi-Player Bandits Revisited—0
Sparsity, variance and curvature in multi-armed bandits—0
Medoids in almost linear time via multi-armed banditsCode0
Multi-Armed Bandits with Metric Movement Costs—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
Trend Detection based Regret Minimization for Bandit Problems—0
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks—0
Variational inference for the multi-armed contextual banditCode0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Efficient Contextual Bandits in Non-stationary Worlds—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Safety-Aware Algorithms for Adversarial Contextual Bandit—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits—0
Efficient Reinforcement Learning via Initial Pure Exploration—0
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration—0
Boltzmann Exploration Done Right—0
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Benchmark Results

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