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 451–500 of 1262 papers

TitleStatusHype
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Expected Improvement-based Contextual Bandits—0
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
Exploration Potential—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits—0
Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits—0
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits—0
Exposure-Aware Recommendation using Contextual Bandits—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
Fair Algorithms for Multi-Agent Multi-Armed Bandits—0
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks—0
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Fair Exploration via Axiomatic Bargaining—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits—0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
Adaptive Exploration in Linear Contextual Bandit—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Faster Maximum Inner Product Search in High Dimensions—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Federated Linear Bandits with Finite Adversarial Actions—0
Federated Linear Contextual Bandits—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Federated Linear Contextual Bandits with User-level Differential Privacy—0
Deep Contextual Multi-armed Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Towards Bayesian Data Selection—0
Federated Online Sparse Decision Making—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Fighting Contextual Bandits with Stochastic Smoothing—0
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Finding the bandit in a graph: Sequential search-and-stop—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Show:102550
← PrevPage 10 of 26Next →

Benchmark Results

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