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 251–275 of 1262 papers

TitleStatusHype
Competing Bandits: The Perils of Exploration Under Competition—0
Balanced Linear Contextual Bandits—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Autonomous Drug Design with Multi-Armed Bandits—0
AutoML for Contextual Bandits—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
Automatic Ensemble Learning for Online Influence Maximization—0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
A Contextual Combinatorial Bandit Approach to Negotiation—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Contextual Bandits with Arm Request Costs and Delays—0
Contextual Bandits Evolving Over Finite Time—0
Contextual Bandits and Optimistically Universal Learning—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Contextual Bandit Applications in Customer Support Bot—0
Asymptotic Randomised Control with applications to bandits—0
Contextual Bandits for adapting to changing User preferences over time—0
Contextual Bandits for Advertising Budget Allocation—0
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)—0
Contextual Bandits for Evaluating and Improving Inventory Control Policies—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
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Benchmark Results

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