Summary

The current state of the art in Machine Learning (ML) and Statistical Inference heavily relies on principles which defy traditional statistical thinking, such as high overparameterization in deep learning and non-convex optimization on high-dimensional landscapes using stochastic descent. This brings the necessity of investigating previously unexplored statistical regimes where classical results do not apply to better understand these novel and impactful practices. As a result, innovative theoretical approaches have emerged, notably through interdisciplinary collaborations, among which those based on high-dimensional statistics, random matrix theory and mathematical physics figure prominently. This thematic trimester therefore aims at fostering the interaction of researchers from several communities working in this fast-developing and emerging area, as well as providing young researchers and students with opportunities for acquiring knowledge on these new theoretical approaches of ML and statistical inference which will play a major role in the future of these disciplines.

Opening colloquium

An opening colloquium about recent developments and challenges of high-dimensional statistical inference and machine learning.

September, 11th 2024, from 3pm to 5pm (+ cocktail)

Speakers : Lenka Zdeborová (EPFL, Switzerland) and Andrea Montanari (Stanford U., USA)

More info and registration: link

Thematic school: Optimization & algorithms for high-dimensional machine learning and inference

This thematic school features mini-courses on the study of high-dimensional (random) optimization landscapes, on the dynamics of optimization algorithms in high dimensions, on approximate message passing algorithms, and related topics.

October, 7th to 11th 2024

List of speakers and subjects:

• François Malgouyres - Properties of the landscape in neural network optimization
• Edouard Pauwels - An introduction to optimization for deep learning
• Valentina Ros - High-dimensional optimization landscapes
• Cynthia Rush - An introduction to approximate message passing algorithms

More info and registration: link

Thematic school: Models & methods for high-dimensional machine learning and inference

This thematic school features mini-courses on tools and techniques for the analysis of high-dimensional models in statistical inference and machine learning.

October, 14th to 18th 2024

List of speakers and subjects:

• Florent Krzakala - Replica method
• Marc Lelarge - Statistical physics and inference
• Zhenyu Liao - Random matrix theory tools for machine learning and inference
• Alexander S.Wein - Mini-course on random tensor models

 More info and registration: link

Workshop: Recent developments beyond classical regimes in statistical learning

This workshop is focused on recent results on high-dimensional (supervised and unsupervised) machine learning and statistical inference. It will in particular involve a round-table debate with top experts on this domain about the major open problems on the field and some promising trends and recent developments.

*This is a joint workshop with the CIMI Thematic Semester "Stochastic control and learning for complex networks"

November 4th to 8th 2024

List of confirmed speakers (other speakers will soon be announced):

• Jean Barbier (ICTP, Italy)
• Giulio Biroli (ENS, France) keynote speaker
• Charles Bordenave (IMM, France)
• Zhou Fan (Yale U., USA)
• Yan Fyodorov (KCL, UK)
• Daniel Hsu (Columbia U., USA)
• Aukosh Jagannath (Waterloo U., Canada)
• Jonathan P. Keating (Oxford U., UK)
• Marc Lelarge (ENS, France)
• Cosme Louart (Hong Kong U., China)
• Bruno Loureiro (ENS, France)
• Pascal Maillard (IMT, France)
• Valentina Ros (Paris-Saclay U., France)
• Subhabrata Sen (Harvard U., USA)
• Beatriz Seoane (Paris-Saclay U., France)
• Inbar Seroussi (Tel-Aviv U., Israel)
• Pragya Sur (Harvard U., USA)
• Malik Tiomoko (Huawei)
• Christos Thrampoulidis (British Columbia U., Canada)
• Pierfrancesco Urbani (IPHT, France)
• Yizhe Zhu (California Irvine U., USA)

 More info and registration: link

Organisation staff: Henrique Goulart (IRIT/Toulouse INP), Vanessa Kientz (CEA List), Vincent Lahoche (CEA List), Xiaoyi Mai (IMT/UT2J), Mohamed Tamaazousti (CEA List)