arXiv Machine Learning

Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards

arXiv:2606. 15146v1 Announce Type: new Abstract: Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives.

arXiv Machine Learning

Jun 29

Learning Peer Influence Probabilities with Linear Contextual Bandits

arXiv:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.

By Ahmed Sayeed Faruk, Mohammad Shahverdikondori, Elena Zheleva

diffusion reinforcement-learning

arXiv AI

Jun 24

Impatient Bandits: Optimizing for the Long-Term Without Delay

arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.

By Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo

reinforcement-learning

arXiv AI

Jul 13

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.

By Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan

llms reinforcement-learning benchmarks

arXiv Machine Learning

Aug 5

LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation

arXiv:2608. 03382v1 Announce Type: cross Abstract: Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation.

By Eugene Lee, Oseong Choi, Byungsoo Kang, Taeyeong Jang

llms reinforcement-learning safety

arXiv Machine Learning

Jul 17

Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

arXiv:2607. 14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention.

By Dingsu Wang, Filip Ryzner, Kelly He, Armando Ordorica, David Woo, Aditya Mantha, Liyao Lu, Usha Amrutha Nookala, Haoran Guo, Jiacong He, Olafur Gudmundsson, Matt Chun, Krystal Benitez, Dhruvil Deven Badani, Yijie Dylan Wang

reinforcement-learning

arXiv AI

Aug 10

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608. 06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Refine to traditional bandit approaches often rely on static options or overlook the saturation effect.

By Shion Ishikawa, Pablo Loyola, Young-joo Chung, Yun Ching Liu

llms rag reinforcement-learning benchmarks

arXiv AI

Jun 9

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries

arXiv:2606. 08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives.

By Linfeng Cao, Ming Shi, Ness B. Shroff

reinforcement-learning

arXiv Machine Learning

Jul 14

Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing

arXiv:2602. 23565v2 Announce Type: replace Abstract: In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best serves them.

By Adhyyan Narang, Sarah Dean, Lillian J Ratliff, Maryam Fazel

efficiency

arXiv AI

1d ago

Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors

arXiv:2608. 16707v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration.

By David Eric Austin, Kaheer Suleman, Jackie Chi Kit Cheung

llms agents reinforcement-learning safety

arXiv AI

Jun 16

Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts

arXiv:2606. 14929v1 Announce Type: cross Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models.

By Yan Dai, Negin Golrezaei, Patrick Jaillet

rag reinforcement-learning safety

arXiv Machine Learning

6d ago

When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits

arXiv:2608. 11560v1 Announce Type: new Abstract: Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning.

By Sang Su Lee, Vineeth Loganathan, Shishir Dash, Vijay Raghavan

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 9

Constrained user-item allocation for e-commerce marketing campaigns

arXiv:2606. 09623v1 Announce Type: new Abstract: When running marketing campaigns, retailers must decide which products to promote and which users to target.

By Maja Lindstr\"om, Natalija Glisovic, Jan von Pichowski, Tommy L\"ofstedt, Martin Rosvall

reinforcement-learning benchmarks safety

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