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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 1262 papers

TitleStatusHype
Competing Bandits: The Perils of Exploration Under Competition—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—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
Balanced Linear Contextual Bandits—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Contextual Multi-Armed Bandits for Causal Marketing—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 Multinomial Logit Bandits with General Value Functions—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
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
Contextual Bandits with Cross-learning—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Asymptotic Randomised Control with applications to bandits—0
Contextual Bandits with Knapsacks for a Conversion Model—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandits with Online Neural Regression—0
Contextual Bandits with Random Projection—0
Contextual Bandits with Side-Observations—0
Contextual Bandits with Similarity Information—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Contextual Bandits with Sparse Data in Web setting—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Contextual Bandits with Stage-wise Constraints—0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual Causal Bayesian Optimisation—0
Context-Aware Bandits—0
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Benchmark Results

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