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 676–700 of 1262 papers

TitleStatusHype
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Causal Contextual Bandits with Targeted Interventions—0
Expected Improvement-based Contextual Bandits—0
Batched Bandits with Crowd Externalities—0
Risk averse non-stationary multi-armed bandits—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits—0
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health—0
Estimation of Warfarin Dosage with Reinforcement LearningCode0
Exploiting Heterogeneity in Robust Federated Best-Arm Identification—0
Improved Algorithms for Misspecified Linear Markov Decision Processes—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
Online Learning for Cooperative Multi-Player Multi-Armed Bandits—0
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations—0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Maximizing and Satisficing in Multi-armed Bandits with Graph InformationCode0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Finite-time Analysis of Globally Nonstationary Multi-Armed BanditsCode0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
An Analysis of Reinforcement Learning for Malaria Control—0
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Benchmark Results

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