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 1226–1250 of 1262 papers

TitleStatusHype
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Conservative Contextual Bandits: Beyond Linear Representations—0
Constant regret for sequence prediction with limited advice—0
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems—0
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget—0
Context-Aware Bandits—0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Contextual Bandit Applications in Customer Support Bot—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Contextual Bandits and Optimistically Universal Learning—0
Contextual Bandits Evolving Over Finite Time—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
Contextual Bandits for Unbounded Context Distributions—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Linear Bandits with Stochastic Delayed Feedback—0
Contextual Bandits with Arm Request Costs and Delays—0
Contextual Bandits with Budgeted Information Reveal—0
Contextual bandits with concave rewards, and an application to fair ranking—0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
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Benchmark Results

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