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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 1201–1250 of 1262 papers

TitleStatusHype
Classical Bandit Algorithms for Entanglement Detection in Parameterized Qubit States—0
Clustered Linear Contextual Bandits with Knapsacks—0
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
Parallel Best Arm Identification in Heterogeneous Environments—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards—0
Combinatorial Pure Exploration of Multi-Armed Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Combinatorial Semi-Bandits with Knapsacks—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
Competing Bandits in Matching Markets—0
Competing Bandits: The Perils of Exploration Under Competition—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
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