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 151–200 of 1262 papers

TitleStatusHype
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsCode0
Multi-armed Bandits with Missing OutcomeCode0
Structure Matters: Dynamic Policy Gradient—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
PageRank Bandits for Link PredictionCode0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints—0
Optimal Streaming Algorithms for Multi-Armed Bandits—0
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits—0
Online Learning for Function Placement in Serverless ComputingCode0
Contextual Bandits with Arm Request Costs and Delays—0
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Stochastic Bandits for Egalitarian Assignment—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Minimax-optimal trust-aware multi-armed bandits—0
Online Posterior Sampling with a Diffusion Prior—0
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs—0
On Lai's Upper Confidence Bound in Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Stabilizing the Kumaraswamy Distribution—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Linear Contextual Bandits with Interference—0
Second Order Bounds for Contextual Bandits with Function Approximation—0
Designing an Interpretable Interface for Contextual Bandits—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Faster Q-Learning Algorithms for Restless Bandits—0
Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network DesignCode0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Representative Arm Identification: A fixed confidence approach to identify cluster representatives—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Online Fair Division with Contextual Bandits—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Show:102550
← PrevPage 4 of 26Next →

Benchmark Results

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