arXiv Machine Learning July 14, 2026 By Adhyyan Narang, Sarah Dean, Lillian J Ratliff, Maryam Fazel

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

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 Machine Learning

Jun 30

SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning

arXiv:2606. 29322v1 Announce Type: new Abstract: Collaborative learning is sustainable only when it benefits each participant.

By Yaron Kiselman, Kfir Y. Levy

safety

arXiv AI

Jun 9

Performative Learning Theory

arXiv:2602. 04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast.

By Julian Rodemann, Unai Fischer-Abaigar, James Bailie, Krikamol Muandet

rag

arXiv Machine Learning

Jun 26

Learning from Equivalence Queries, Revisited

arXiv:2604. 04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates.

By Mark Braverman, Roi Livni, Yishay Mansour, Shay Moran, Kobbi Nissim

reinforcement-learning safety

arXiv Machine Learning

Jun 16

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.

By Ahmed Sayeed Faruk, Elena Zheleva

reinforcement-learning

arXiv AI

Aug 12

Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons

arXiv:2608. 10045v1 Announce Type: cross Abstract: The problem of learning from pairwise comparisons has been widely studied across many domains such as recommendation systems, social choice, and more recently, fine-tuning large language models.

By Kaustubh Shivshankar Shejole, Tanish Agarwal, Arpit Agarwal, Avishek Ghosh

llms fine-tuning safety

Related stories

Learning Peer Influence Probabilities with Linear Contextual Bandits

SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning

Performative Learning Theory

Learning from Equivalence Queries, Revisited

Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards

Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons