Machine learning theory has long focused on classical supervised learning settings, where a model is trained on input–label pairs drawn from a well-defined data distribution, with the aim of achieving low test error on such distribution. Despite remarkable advances in such settings, recent breakthroughs in generative AI have transformed our understanding of generalization, revealing phenomena such as emergent capabilities and in-context learning, which lie beyond the scope of existing theoretical frameworks. These empirical developments call for new theoretical paradigms, fostering closer interactions between theoreticians and practitioners to address the distinctive challenges posed by generative models.
The purpose of this workshop is to bring together diverse theory-oriented communities to articulate and synthesize core principles underlying modern generative AI, and to outline the central challenges in advancing our scientific understanding.
Date: December XXth, 2026
Location: Palais des Congrès, Paris, France
10:00--10:30: Contributed talks: TBC
Contributed Talk 1: TBC
Contributed Talk 2: TBC
10:30--11:30: Break/Poster Session 1 (see detailed planning)
11:30--12:15: Plenary talk 2: TBC
(12:15--13:45) Lunch break
Poster size: A0 portrait or A1 landscape
Contact: prigm-neurips-2026@googlegroups.com