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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 901–950 of 1262 papers

TitleStatusHype
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Small-loss bounds for online learning with partial information—0
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness—0
SmartChoices: Augmenting Software with Learned Implementations—0
Smoothed Online Learning is as Easy as Statistical Learning—0
Smooth Sequential Optimisation with Delayed Feedback—0
Social Learning in Multi Agent Multi Armed Bandits—0
Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates—0
Sparse Nonparametric Contextual Bandits—0
Sparsity, variance and curvature in multi-armed bandits—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits—0
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes—0
Stabilizing the Kumaraswamy Distribution—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Statistical Inference with M-Estimators on Adaptively Collected Data—0
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits—0
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond—0
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling—0
Stochastic Bandits for Egalitarian Assignment—0
Stochastic Bandits with Linear Constraints—0
Stochastic Bandits with Vector Losses: Minimizing ^-Norm of Relative Losses—0
Stochastic Contextual Bandits with Graph-based Contexts—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
Stochastic Contextual Bandits with Known Reward Functions—0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits—0
Stochastic Graph Bandit Learning with Side-Observations—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
Stochastic Multi-armed Bandits in Constant Space—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Achieving Fairness in Stochastic Multi-armed Bandit Problem—0
Stochastic Multi-Armed Bandits with Control Variates—0
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System—0
Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm—0
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach—0
Stochastic Neural Network with Kronecker Flow—0
Strategic Linear Contextual Bandits—0
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk—0
Streaming Algorithms for Stochastic Multi-armed Bandits—0
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis—0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
Structure Matters: Dynamic Policy Gradient—0
Sublinear Optimal Policy Value Estimation in Contextual Bandits—0
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making—0
Survey Bandits with Regret Guarantees—0
Taking a hint: How to leverage loss predictors in contextual bandits?—0
Target Tracking for Contextual Bandits: Application to Demand Side Management—0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
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Benchmark Results

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