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
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Achieving User-Side Fairness in Contextual Bandits—0
A Classification View on Meta Learning Bandits—0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
A Contextual Combinatorial Bandit Approach to Negotiation—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
A Correction of Pseudo Log-Likelihood Method—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Active Search for Sparse Signals with Region Sensing—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Adapting to Misspecification in Contextual Bandits—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Adaptive Data Augmentation for Thompson Sampling—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
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Benchmark Results

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