Portrait of Pablo Piantanida

Pablo Piantanida

Associate Academic Member
Full Professor, Université Paris-Saclay
Director, International Laboratory on Learning Systems (ILLS), McGill University
Associate professor, École de technologie supérieure (ETS), Department of Systems Engineering
Research Topics
AI Safety
Information Theory
Machine Learning Theory
Natural Language Processing
Website
Google Scholar
LinkedIn

Biography

I am a professor at CentraleSupélec (Université Paris-Saclay) with the French National Centre for Scientific Research (CNRS), and Director of the International Laboratory on Learning Systems (ILLS) which gathers McGill University, École de technologie supérieure (ÉTS), Mila – Quebec AI Institute, France’s Centre Nationale de la Recherche Scientifique (CNRS), Université Paris-Saclay, and the École CentraleSupélec.

My research revolves around the application of advanced statistical and information-theoretic techniques to the field of machine learning. I am interested in developing rigorous techniques based on information measures and concepts for building safe and trustworthy AI systems and establishing confidence in their behavior and robustness, thereby securing their use in society. My primary areas of expertise include information theory, information geometry, learning theory, privacy, fairness, with applications to computer vision and natural language processing.

I obtained my undergraduate education at the University of Buenos Aires and pursued graduate studies in applied mathematics at Paris-Saclay University in France. Throughout my career, I have also held visiting positions at INRIA, Université de Montréal and Ecole de Technologie Supérieure (ÉTS), among others.

My earlier research encompassed the fields of information theory beyond distributed compression, statistical decision, universal source coding, cooperation, feedback, index coding, key generation, security, and privacy, among others.

I teach courses on machine learning, information theory and deep learning, covering topics such as statistical learning theory, information measures, statistical principles of neural networks.

Current Students

Chataigner Cléa
PhD - McGill University
Principal supervisor :
Golnoosh Farnadi
Maxime Darrin
PhD - McGill University
Principal supervisor :
Jackie Cheung
Website
Github
Google Scholar
Zahra Dehghani Tafti
PhD
Matthieu Dubois
Collaborating researcher - Sorbonne université
Philippe Formont
PhD - École de technologie suprérieure
Github
Google Scholar
Langlois Henri
Master's Research - Paris-Saclay University
Co-supervisor :
Jackie Cheung
Github
Fanny JOURDAN
Postdoctorate - École de technologie suprérieure
Co-supervisor :
Jackie Cheung
Website
Google Scholar
Ricard Marxer
Collaborating researcher - University of Toulon
Co-supervisor :
Mirco Ravanelli
Website
Github
Google Scholar
Chiara Roverato
PhD - McGill University
Principal supervisor :
Mark Coates
Matteo Sammut
PhD - Université Paris Dauphine-PSL
Github
Nicolas Thome
Collaborating researcher - Sorbonne Université
Website
Google Scholar

Publications

Multiple-model coding scheme for electrical signal compression
Corentin Presvôts
Michel Kieffer
Thibault Prevost
Patrick Panciatici
Zuxing Li
Pablo Piantanida
2025-04-01
Signal Processing (published)
doi.org
Multiple-model coding scheme for electrical signal compression
Corentin Presvôts
Michel Kieffer
Thibault Prevost
Patrick Panciatici
Zuxing Li
Pablo Piantanida
2025-04-01
Signal Processing (published)
doi.org
A Strong Baseline for Molecular Few-Shot Learning
Philippe Formont
Hugo Jeannin
Pablo Piantanida
Ismail Ben Ayed
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving con… (see more)voluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.
2025-02-15
TMLR (accepted)
openreview.net
openreview.net
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais
Philippe Formont
Pablo Piantanida
Elisabeth Gassiat
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance l… (see more)evels comparable to the original models. In this work, we investigate the impact of quantization procedures on the privacy of data-driven models, specifically focusing on their vulnerability to membership inference attacks. We derive an asymptotic theoretical analysis of Membership Inference Security (MIS), characterizing the privacy implications of quantized algorithm weights against the most powerful (and possibly unknown) attacks. Building on these theoretical insights, we propose a novel methodology to empirically assess and rank the privacy levels of various quantization procedures. Using synthetic datasets, we demonstrate the effectiveness of our approach in assessing the MIS of different quantizers. Furthermore, we explore the trade-off between privacy and performance using real-world data and models in the context of molecular modeling.
2025-02-10
ArXiv (preprint)
arxiv.org
arxiv.org
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais
Philippe Formont
Pablo Piantanida
Elisabeth Gassiat
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance l… (see more)evels comparable to the original models. In this work, we investigate the impact of quantization procedures on the privacy of data-driven models, specifically focusing on their vulnerability to membership inference attacks. We derive an asymptotic theoretical analysis of Membership Inference Security (MIS), characterizing the privacy implications of quantized algorithm weights against the most powerful (and possibly unknown) attacks. Building on these theoretical insights, we propose a novel methodology to empirically assess and rank the privacy levels of various quantization procedures. Using synthetic datasets, we demonstrate the effectiveness of our approach in assessing the MIS of different quantizers. Furthermore, we explore the trade-off between privacy and performance using real-world data and models in the context of molecular modeling.
2025-02-10
ArXiv (preprint)
doi.org
arxiv.org