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

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Showing 426–450 of 1262 papers

TitleStatusHype
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback—0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Queue Scheduling with Adversarial Bandit Learning—0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards—0
Approximately Stationary Bandits with Knapsacks—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
On Differentially Private Federated Linear Contextual Bandits—0
Kernel Conditional Moment Constraints for Confounding Robust InferenceCode0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments—0
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond—0
Practical Contextual Bandits with Feedback Graphs—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
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Benchmark Results

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