3 papers
cs.IR2025
LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems
Baptiste Bonin, Maxime Heuillet, Audrey Durand
Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) research. We investigate how Large Languag…
cs.LG2025
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Jonas Ngnawé, Maxime Heuillet, Sabyasachi Sahoo +5
Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims to simultaneously achieve adaptation to…
cs.IR2025
Preference-based learning for news headline recommendation
Alexandre Bouras, Audrey Durand, Richard Khoury
This study explores strategies for optimizing news headline recommendations through preference-based learning. Using real-world data of user interactions with French-language onlin…