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 601–650 of 1262 papers

TitleStatusHype
Approximate Function Evaluation via Multi-Armed Bandits—0
Reinforced Meta Active Learning—0
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits—0
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits—0
Restless Multi-Armed Bandits under Exogenous Global Markov Process—0
Federated Online Sparse Decision Making—0
Truncated LinUCB for Stochastic Linear BanditsCode0
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Efficient Kernel UCB for Contextual BanditsCode0
Shuffle Private Linear Contextual Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Remote Contextual Bandits—0
Smoothed Online Learning is as Easy as Statistical Learning—0
Budgeted Combinatorial Multi-Armed Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—0
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks—0
Adaptive Experimentation with Delayed Binary FeedbackCode0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Neural Collaborative Filtering Bandits via Meta Learning—0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms—0
Top-K Ranking Deep Contextual Bandits for Information Selection Systems—0
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)—0
Modelling Cournot Games as Multi-agent Multi-armed Bandits—0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits—0
Safe Linear Leveling Bandits—0
Privacy Amplification via Shuffling for Linear Contextual Bandits—0
Efficient Action Poisoning Attacks on Linear Contextual Bandits—0
Best Arm Identification under Additive Transfer Bandits—0
Contextual Bandit Applications in Customer Support Bot—0
On Submodular Contextual Bandits—0
Bandits with Knapsacks beyond the Worst Case—0
Identification of the Generalized Condorcet Winner in Multi-dueling BanditsCode0
Optimal Algorithms for Stochastic Contextual Preference Bandits—0
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and LearningCode0
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization—0
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Bandits—0
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits—0
Show:102550
← PrevPage 13 of 26Next →

Benchmark Results

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