Portrait de Pablo Piantanida

Pablo Piantanida

Membre académique associé
Professeur titulaire, Université Paris-Saclay
Directeur, Laboratoire international sur les systèmes d'apprentissage (ILLS), McGill University
Professeur associé, École de technologie supérieure (ETS), Département de génie des systèmes
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Biographie

Je suis professeur au CentraleSupélec de l'Université Paris-Saclay avec le Centre national français de la recherche scientifique (CNRS), et directeur du Laboratoire international sur les systèmes d'apprentissage (ILLS) qui regroupe l'Université McGill, l'École de technologie supérieure (ÉTS), Mila - Institut québécois d'intelligence artificielle, le Centre national français de la recherche scientifique (CNRS), l'Université Paris-Saclay et l'École CentraleSupélec.

Mes recherches portent sur l'application de techniques statistiques et de théorie de l'information avancées au domaine de l'apprentissage automatique. Je m'intéresse au développement de techniques rigoureuses basées sur des mesures et des concepts d'information pour construire des systèmes d'IA sûrs et fiables et établir la confiance dans leur comportement et leur robustesse, sécurisant ainsi leur utilisation dans la société. Mes principaux domaines d'expertise sont la théorie de l'information, la géométrie de l'information, la théorie de l'apprentissage, la protection de la vie privée, l'équité, avec des applications à la vision par ordinateur et au traitement du langage naturel.

J'ai fait mes études de premier cycle à l'université de Buenos Aires et j'ai poursuivi des études supérieures en mathématiques appliquées à l'université Paris-Saclay en France. Tout au long de ma carrière, j'ai également occupé des postes d'invité à l'INRIA, à l'Université de Montréal et à l'École de technologie supérieure (ÉTS), entre autres.

Mes recherches antérieures ont porté sur les domaines de la théorie de l'information au-delà de la compression distribuée, de la décision statistique, du codage universel des sources, de la coopération, de la rétroaction, du codage d'index, de la génération de clés, de la sécurité et de la protection des données.

Je donne des cours sur l'apprentissage automatique, la théorie de l'information et l'apprentissage profond, couvrant des sujets tels que la théorie de l'apprentissage statistique, les mesures de l'information, les principes statistiques des réseaux neuronaux.

Étudiants actuels

Publications

Open-Set Likelihood Maximization for Few-Shot Learning
Malik Boudiaf
Etienne Bennequin
Myriam Tami
Antoine Toubhans
Pablo Piantanida
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 … (voir plus)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.
2023-06-17
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
Eduardo Dadalto Câmara Gomes
Pierre Colombo
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
2023-06-06
ArXiv (prépublication)
doi.org
arxiv.org
On the incompatibility of accuracy and equal opportunity
Carlos Pinzón
Catuscia Palamidessi
Pablo Piantanida
Frank Valencia
2023-05-02
Machine-mediated learning (publié)
doi.org
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection
Marine Picot
Federica Granese
Guillaume Staerman
Marco Romanelli
Francisco Messina
Pablo Piantanida
Pierre Colombo
2023-03-28
TMLR (accepté)
openreview.net
openreview.net
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Maxime Darrin
Guillaume Staerman
Eduardo Dadalto Câmara Gomes
Jackie Ck Cheung
Pablo Piantanida
Pierre Colombo
2023-02-20
ArXiv (prépublication)
doi.org
arxiv.org
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese
Marco Romanelli
Siddharth Garg
Pablo Piantanida
2023-02-04
ArXiv (prépublication)
doi.org
arxiv.org
On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper)
Daniele Gorla
Louis Jalouzot
Federica Granese
Catuscia Palamidessi
Pablo Piantanida
We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism … (voir plus)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.
2023-01-01
ICTCS (publié)
dblp.uni-trier.de
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
Nuno Miguel Guerreiro
Pierre Colombo
Pablo Piantanida
André Martins
Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can un… (voir plus)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.
2022-12-19
ArXiv (prépublication)
doi.org
arxiv.org
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Pierre Colombo
Eduardo Dadalto Câmara Gomes
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
2022-11-24
ArXiv (prépublication)
doi.org
arxiv.org
Beyond Mahalanobis Distance for Textual OOD Detection
Pierre Colombo
Eduardo Dadalto Câmara Gomes
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
openreview.net
KNIFE: Kernelized-Neural Differential Entropy Estimation
Georg Pichler
Pierre Colombo
Malik Boudiaf
Gunther Koliander
Pablo Piantanida
Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when l… (voir plus)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 .
2022-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Realistic Evaluation of Transductive Few-Shot Learning - Supplementary Material
Olivier Veilleux
Éts Montréal
Malik Boudiaf
Pablo Piantanida
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-… (voir plus)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.
2022-01-01
(publié)
www.semanticscholar.org