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

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Showing 1026–1050 of 1262 papers

TitleStatusHype
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization—0
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
Provably and Practically Efficient Neural Contextual Bandits—0
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks—0
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback—0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Provably Optimal Algorithms for Generalized Linear Contextual Bandits—0
Pure Exploration in Asynchronous Federated Bandits—0
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler—0
Combinatorial Pure Exploration of Causal Bandits—0
Pure Exploration under Mediators' Feedback—0
QoS-Aware Multi-Armed Bandits—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
Quantum contextual bandits and recommender systems for quantum data—0
Quantum Heavy-tailed Bandits—0
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets—0
Query-Efficient Correlation Clustering with Noisy Oracle—0
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
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Benchmark Results

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