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 326–350 of 1262 papers

TitleStatusHype
Corruption-robust exploration in episodic reinforcement learning—0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Query-Reward Tradeoffs in Multi-Armed Bandits—0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback—0
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits—0
Data Poisoning Attacks in Contextual Bandits—0
Data Poisoning Attacks on Stochastic Bandits—0
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees—0
Batched Nonparametric Bandits via k-Nearest Neighbor UCB—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Context-Aware Bandits—0
Decentralized Exploration in Multi-Armed Bandits -- Extended version—0
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits—0
Decentralized Multi-player Multi-armed Bandits with No Collision Information—0
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits—0
Decision Automation for Electric Power Network Recovery—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Show:102550
← PrevPage 14 of 51Next →

Benchmark Results

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