35 citations · 35 across the 4 of their papers we have counts for
6 papers
Amortizing Sequential Experimental Design in Reasoning LLMs
Anita Kriz, Antonin Berthon, Nicolás Astorga +1
Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks…
On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics
Julianna Piskorz, Antonin Berthon, Mihaela van der Schaar
On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing comparisons between supervise…
Skill Neologisms: Towards Skill-based Continual Learning
Antonin Berthon, Nicolas Astorga, Mihaela van der Schaar
Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly. However, extending model capabilities to new skills in a scalable ma…
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon +1
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often st…
Language Bottleneck Models for Qualitative Knowledge State Modeling
Antonin Berthon, Mihaela van der Schaar
Accurately assessing student knowledge is central to education. Cognitive Diagnosis (CD) models estimate student proficiency at a fixed point in time, while Knowledge Tracing (KT)…
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon, Bo Han, Gang Niu +2
In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise…