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

PhD - McGill University

Principal supervisor :

Jackie Cheung

maxime.darrin@mila.quebec

PhD - École de technologie suprérieure

zahra.dehghani-tafti@mila.quebec

philippe.formont@mila.quebec

Philippe Formont

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

RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data

Maxime Darrin

Pierre Colombo

Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots… (see more), is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.

2023-10-06

EMNLP/2023/Conference (accepted)

openreview.net

Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation

Nuno M. Guerreiro

Pierre Colombo

André Martins

Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can un… (see more)predictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. It becomes thus crucial to implement effective preventive strategies to guarantee their proper functioning. In this paper, we address the problem of hallucination detection in NMT by following a simple intuition: as hallucinations are detached from the source content, they exhibit encoder-decoder attention patterns that are statistically different from those of good quality translations. We frame this problem with an optimal transport formulation and propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. Experimental results show that our detector not only outperforms all previous model-based detectors, but is also competitive with detectors that employ external models trained on millions of samples for related tasks such as quality estimation and cross-lingual sentence similarity.

2023-06-30

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

Open-Set Likelihood Maximization for Few-Shot Learning

Malik Boudiaf

Etienne Bennequin

Myriam Tami

Antoine Toubhans

Celine Hudelot

Ismail Ben Ayed

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few … (see more)labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation Open-Set Likelihood Optimization (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection. Code is available at https://github.com/ebennequin/few-shot-open-set.

2023-06-16

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection

Eduardo Dadalto Câmara Gomes

Pierre Colombo

Guillaume Staerman

Nathan Noiry

2023-06-05

ArXiv (preprint)

A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection

Marine Picot

Federica Granese

Guillaume Staerman

Marco Romanelli

Francisco Messina

Pierre Colombo

2023-03-27

TMLR (accepted)

openreview.net

A Minimax Approach Against Multi-Armed Adversarial Attacks Detection

Federica Granese

Marco Romanelli

Siddharth Garg

2023-02-03

ArXiv (preprint)

On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper)

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 delve into the investigation of two notions of local differential privacy (LDP), namely 𝜀 -LDP and Rényi LDP. On one hand, we prove that, without any assumption on the underlying distributions, it is not possible to have an algorithm able to infer the level of data protection with provable guarantees. On the other hand, we demonstrate that, under reasonable assumptions (namely, Lipschitzness of the involved densities on a closed interval), such guarantees exist and can be achieved by a simple histogram-based estimator.

2022-12-31

ICTCS (published)

dblp.uni-trier.de

Beyond Mahalanobis-Based Scores for Textual OOD Detection

Pierre Colombo

Eduardo D. C. Gomes

Guillaume Staerman

Nathan Noiry

2022-11-28

Conference on Neural Information Processing Systems (Accept)

openreview.net

KNIFE: Kernelized-Neural Differential Entropy Estimation

Georg Pichler

Pierre Colombo

Malik Boudiaf

Gunther Koliander

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when l… (see more)earn dis-entangled or compressed representations of high dimensional data. However, differential entropy (DE), another fundamental measure of information, has not found widespread use in neural network training. Although DE offers a potentially wider range of applications than MI, off-the-shelf DE estimators are either non differentiable, computationally intractable or fail to adapt to changes in the underlying distribution. These drawbacks prevent them from being used as regularizers in neural networks training. To address shortcomings in previously proposed estimators for DE, here we introduce K NIFE , a fully parameterized, differentiable kernel-based estimator of DE. The ﬂexibility of our approach also allows us to construct K NIFE -based estimators for conditional (on either discrete or continuous variables) DE, as well as MI. We empirically validate our method on high-dimensional synthetic data and further apply it to guide the training of neural networks for real-world tasks. Our experiments on a large variety of tasks, including visual domain adaptation, textual fair classiﬁcation, and textual ﬁne-tuning demonstrate the effectiveness of K NIFE - based estimation. Code can be found at https: //github.com/g-pichler/knife .

2021-12-31

arXiv.org (preprint)

dblp.uni-trier.de

Realistic Evaluation of Transductive Few-Shot Learning - Supplementary Material

Olivier Veilleux

Éts Montréal

Malik Boudiaf

Ismail Ben

Ayed Éts Montreal

In the main tables of the paper, we did not include the performances of α-TIM in the standard balanced setting. Here, we emphasize that α-… (see more)TIM is a generalization of TIM [1] as when α → 1 (i.e., the α-entropies tend to the Shannon entropies), α-TIM tends to TIM. Therefore, in the standard setting, where optimal hyper-parameter α is obtained over validation tasks that are balanced (as in the standard validation tasks of the original TIM and the other existing methods), the performance of α-TIM is the same as TIM. When α is tuned on balanced validation tasks, we obtain an optimal value of α very close to 1, and our α-mutual information approaches the standard mutual information. When the validation tasks are uniformly random, as in our new setting and in the validation plots we provided in the main figure, one can see that the performance of α-TIM remains competitive when we tend to balanced testing tasks (i.e., when a is increasing), but is significantly better than TIM when we tend to uniformly-random testing tasks (a = 1). These results illustrate the flexibility of α-divergences, and are in line with the technical analysis provided in the main paper.

2021-12-31

(published)

www.semanticscholar.org

Learning Anonymized Representations with Adversarial Neural Networks

Clément Feutry

Yoshua Bengio

P. Duhamel

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals… (see more) as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).

2018-02-25

ArXiv (preprint)