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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 851–900 of 1262 papers

TitleStatusHype
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality—0
Multi-Objective Generalized Linear Bandits—0
Multi-Player Approaches for Dueling Bandits—0
Multi-Player Bandits: A Trekking Approach—0
Multi-Player Bandits Revisited—0
Multi-Player Bandits Robust to Adversarial Collisions—0
Multiplayer Information Asymmetric Contextual Bandits—0
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions—0
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions—0
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms—0
Multiplier Bootstrap-based Exploration—0
MultiScale Contextual Bandits for Long Term Objectives—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-Task Learning for Contextual Bandits—0
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access—0
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Nearest Neighbor Search Under Uncertainty—0
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions—0
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information—0
Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits—0
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration—0
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem—0
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback—0
Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits with Known Optimality Gap—0
Near Optimal Best Arm Identification for Clustered Bandits—0
Near-Optimal Private Learning in Linear Contextual Bandits—0
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies—0
Neural Bandit with Arm Group Graph—0
Neural Collaborative Filtering Bandits via Meta Learning—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Neural Contextual Bandits for Personalized Recommendation—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits—0
Neural Contextual Bandits with Deep Representation and Shallow Exploration—0
Neural Network Retraining for Model Serving—0
Neural Risk-sensitive Satisficing in Contextual Bandits—0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits—0
Nonparametric Contextual Bandits in an Unknown Metric Space—0
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Nonparametric Stochastic Contextual Bandits—0
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
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Benchmark Results

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