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.

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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

One email a morning, machine-written

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