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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

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Showing 801–850 of 1262 papers

TitleStatusHype
Queue Scheduling with Adversarial Bandit Learning—0
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms—0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Random Effect Bandits—0
Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards—0
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback—0
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Rate-Constrained Remote Contextual Bandits—0
Reciprocal Learning—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Reducing Dueling Bandits to Cardinal Bandits—0
Regional Multi-Armed Bandits—0
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback—0
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond—0
Regularized Contextual Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit—0
Reinforced Meta Active Learning—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates—0
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach—0
Remote Contextual Bandits—0
Replicability is Asymptotically Free in Multi-armed Bandits—0
Representation-Driven Reinforcement Learning—0
Representative Arm Identification: A fixed confidence approach to identify cluster representatives—0
Replicable Bandits—0
Residual Bootstrap Exploration for Bandit Algorithms—0
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication—0
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism—0
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits—0
Resourceful Contextual Bandits—0
Restless Multi-Armed Bandits under Exogenous Global Markov Process—0
Restless Multi-armed Bandits under Frequency and Window Constraints for Public Service Inspections—0
Revisiting Simple Regret: Fast Rates for Returning a Good Arm—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits—0
Reward Teaching for Federated Multi-armed Bandits—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
Risk averse non-stationary multi-armed bandits—0
Risk-Aversion in Multi-armed Bandits—0
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Benchmark Results

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