Pablo Piantanida

clea.chataigner@mila.quebec

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@mila.quebec

PhD - McGill University

Principal supervisor :

Jackie Cheung

PhD - École de technologie suprérieure

zahra.dehghani-tafti@mila.quebec

philippe.formont@mila.quebec

PhD - École de technologie suprérieure

fanny.jourdan@mila.quebec

Fanny JOURDAN

Postdoctorate - École de technologie suprérieure

Co-supervisor :

Jackie Cheung

Website

guillermo.martin@mila.quebec

Guillermo Martin de Paula

PhD - Université Paris-Saclay

Ricard Marxer

Collaborating researcher - University of Toulon

Co-supervisor :

Mirco Ravanelli

ricard.marxer@mila.quebec

PhD - McGill University

Principal supervisor :

Mark Coates

chiara.roverato@mila.quebec

Matteo Sammut

PhD - Université Paris Dauphine-PSL

matteo.sammut@dauphine.eu

Amin Sghaier

Master's Research - École de technologie suprérieure

Co-supervisor :

Marco Pedersoli

amin.sghaier@mila.quebec

Nicolas Thome

Collaborating researcher - Sorbonne Université

thome.nicolas@gmail.com

Website

Publications

Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

Arthur SATOUF

Gabriel Ben-Zenou

Benjamin Piwowarski

Habiboulaye Amadou-Boubacar

Current sparse neural information retrieval (IR) methods, and to a lesser extent more traditional models such as BM25, do not take into acco… (see more)unt the document collection and the complex interplay between different term weights when representing a single document. In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case -- and in particular to the high number of potential features (here, tokens). RSA dynamically modulates token-document interactions by considering the influence of other documents in the dataset, better contrasting document representations. Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and achieves state-of-the-art performance on out-of-domain datasets from the BEIR benchmark. https://github.com/arthur-75/Rational-Retrieval-Acts

2025-07-12

International ACM SIGIR Conference on Research and Development in Information Retrieval (published)

Multiple-model coding scheme for electrical signal compression

Corentin Presvôts

Michel Kieffer

Thibault Prevost

Patrick Panciatici

Zuxing Li

2025-03-31

Signal Processing (published)

A Strong Baseline for Molecular Few-Shot Learning

Hugo Jeannin

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.

2025-02-14

TMLR (accepted)

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

Eric Aubinais

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.

2025-02-09

ArXiv (preprint)

On Estimating the Strength of Differentially Private Mechanisms in a Black-Box Setting

Daniele Gorla

Louis Jalouzot

Federica Granese

Catuscia Palamidessi

We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism … (see more)is a priori unknown to them (the so-called “black-box” scenario). In particular, we explore four notions of differential privacy, namely local/central

2024-12-31

IEEE Transactions on Dependable and Secure Computing (unknown)

Unsupervised Layer-wise Score Aggregation for Textual OOD Detection

Guillaume Staerman

Eduardo DC Gomez

Jackie CK Cheung

Pierre Colombo

Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number… (see more) of AI-based systems. Existing OOD textual detectors often rely on an anomaly score (e.g., Mahalanobis distance) computed on the embedding output of the last layer of the encoder. In this work, we observe that OOD detection performance varies greatly depending on the task and layer output. More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer were picked. To leverage this observation, we propose a data-driven, unsupervised method to combine layer-wise anomaly scores. In addition, we extend classical textual OOD benchmarks by including classification tasks with a greater number of classes (up to 77), which reflects more realistic settings. On this augmented benchmark, we show that the proposed post-aggregation methods achieve robust and consistent results while removing manual feature selection altogether. Their performance achieves near oracle's best layer performance.

2024-12-30

OpenReview (published)

When is an Embedder More Promising than Another?

Ismail Ben Ayed

Jackie Chi Kit Cheung

Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to p… (see more)erform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primarily because of the lack of a standardized framework for comparison. However, acquiring adequately large and representative datasets for conducting these assessments is not always viable and can prove to be prohibitively expensive and time-consuming. In this paper, we present a unified approach to evaluate embedders. First, we establish theoretical foundations for comparing embedding models, drawing upon the concepts of sufficiency and informativeness. We then leverage these concepts to devise a tractable comparison criterion (information sufficiency), leading to a task-agnostic and self-supervised ranking procedure. We demonstrate experimentally that our approach aligns closely with the capability of embedding models to facilitate various downstream tasks in both natural language processing and molecular biology. This effectively offers practitioners a valuable tool for prioritizing model trials.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection

Eduardo Dadalto Câmara Gomes

Florence Alberge

Pierre Duhamel

2024-07-09

TMLR (accepted)

Beyond the Norms: Detecting Prediction Errors in Regression Models

Andres Altieri

Marco Romanelli

Georg Pichler

Florence Alberge

This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g.… (see more), aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regressor exceeds a specified discrepancy (or error). Then, using powerful tools for probabilistic modeling, we estimate the discrepancy density, and we measure its statistical diversity using our proposed metric for statistical dissimilarity. In turn, this allows us to derive a data-driven score that expresses the uncertainty of the regression outcome. We show empirical improvements in error detection for multiple regression tasks, consistently outperforming popular baseline approaches, and contributing to the broader field of uncertainty quantification and safe machine learning systems.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning

Georg Pichler

Marco Romanelli

Leonardo Rey Vega

Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data i… (see more)s ever exchanged either between the clients or between the clients and the central server. In this paper, we challenge this claim by introducing a simple but still very effective membership inference attack algorithm, which relies only on a single training step. In contrast to the popular honest-but-curious model, we investigate a framework with a dishonest central server. Our strategy is applicable to models with ReLU activations and uses the properties of this activation function to achieve perfect accuracy. Empirical evaluation on visual classification tasks with MNIST, CIFAR10, CIFAR100 and CelebA datasets show that our method provides perfect accuracy in identifying one sample in a training set with thousands of samples. Occasional failures of our method lead us to discover duplicate images in the CIFAR100 and CelebA datasets.

2024-06-30

IEEE Transactions on Dependable and Secure Computing (published)

GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews

Ines Arous

Jackie CK Cheung

Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to confere… (see more)nces has strained the reviewing process. This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer's main arguments as part of their decision process. In this paper, we introduce \sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews. Unlike traditional consensus-based methods, \sys extracts both common and unique opinions from the reviews. We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews. Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions. Our experimental results with both automatic metrics and human evaluation show that \sys generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.

2024-06-10

ArXiv (preprint)

Optimal Zero-Shot Detector for Multi-Armed Attacks

Federica Granese

Marco Romanelli

This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them va… (see more)rious avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting any alterations to the input. We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker. Specifically, the defender is unable to utilize any data samples for training a defense model or verifying the integrity of the channel. Instead, the defender relies exclusively on a set of pre-existing detectors readily available"off the shelf". To tackle this challenge, we derive an innovative information-theoretic defense approach that optimally aggregates the decisions made by these detectors, eliminating the need for any training data. We further explore a practical use-case scenario for empirical evaluation, where the attacker possesses a pre-trained classifier and launches well-known adversarial attacks against it. Our experiments highlight the effectiveness of our proposed solution, even in scenarios that deviate from the optimal setup.

2024-04-17

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)