Portrait de Alexandre Drouin

Alexandre Drouin

Membre industriel associé
Professeur adjoint, Université Laval, Département de génie électrique et de génie informatique
Chercheur scientifique, ServiceNow
Sujets de recherche
Agent basé sur un LLM
Apprentissage profond
Biologie computationnelle
Causalité
Prévision des séries temporelles
Site web
Google Scholar
LinkedIn
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Biographie

Alexandre Drouin est chercheur en intelligence artificielle chez ServiceNow Research à Montréal et professeur associé au Département d’informatique et de génie logiciel de l’Université Laval. Il dirige une équipe de recherche qui explore l’utilisation de l’apprentissage automatique pour la prise de décision dans des environnements dynamiques complexes. Son intérêt de recherche principal est la prise de décision causale, dont le but est de répondre à des questions interventionnelles et contrefactuelles en tenant compte des sources d’incertitude potentielles, par exemple l’ambiguïté des relations causales sous-jacentes à un système et l’effet de variables latentes. Il s’intéresse aussi aux modèles de prédiction probabiliste pour les séries temporelles et à leur utilisation pour prédire l’effet à long terme d’actions.

Il est détenteur d’un doctorat en informatique de l’Université Laval, qu’il a reçu pour son travail sur le développement d’algorithmes d’apprentissage automatique pour la découverte de biomarqueurs en génomique et leur application au problème de résistance aux antibiotiques.

Étudiants actuels

Arjun Ashok
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Irina Rish
Site web
Github
Google Scholar
Léo Boisvert
Doctorat - Polytechnique
Co-superviseur⋅e :
Quentin Cappart
Philippe Brouillard
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Dhanya Sridhar
philippebrouillard@gmail.com
Site web
Github
Google Scholar
Oscar Clivio
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Ioannis Mitliagkas
Site web
Github
Google Scholar
Thibaud Godon
Doctorat - Université Laval
Superviseur⋅e principal⋅e :
Pascal Germain
Github
Google Scholar

Publications

Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Biloš
Hena Ghonia
N. Hassen
Anderson Schneider
Sahil Garg
Alexandre Drouin
Nicolas Chapados
Yuriy Nevmyvaka
Irina Rish
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama , a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen “out-of-distribution” time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws [7] to fit and predict model scaling behavior. The open source code is made available at https://github
2023-01-01
arXiv.org (prépublication)
doi.org
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
Jacques Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (voir plus)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
2022-08-11
ArXiv (prépublication)
doi.org
arxiv.org
TACTiS: Transformer-Attentional Copulas for Time Series
Alexandre Drouin
Étienne Marcotte
Nicolas Chapados
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.
2022-02-07
ArXiv (prépublication)
arxiv.org
arxiv.org
TACTiS: Transformer-Attentional Copulas for Time Series
Alexandre Drouin
Étienne Marcotte
Nicolas Chapados
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.
2022-01-01
ICML (publié)
proceedings.mlr.press
arxiv.org
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites
Faizy Ahsan
Alexandre Drouin
Franccois Laviolette
Doina Precup
Mathieu Blanchette
The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant met… (voir plus)hodological developments from the field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits of the a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.
2020-12-16
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)
doi.org
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Alexandre Drouin
Discovering causal relationships in data is a challenging task that involves solving a combinatorial problem for which the solution is not a… (voir plus)lways identifiable. A new line of work reformulates the combinatorial problem as a continuous constrained optimization one, enabling the use of different powerful optimization techniques. However, methods based on this idea do not yet make use of interventional data, which can significantly alleviate identifiability issues. In this work, we propose a neural network-based method for this task that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
arxiv.org
G RADIENT -B ASED N EURAL DAG L EARNING WITH I NTERVENTIONS
Philippe Brouillard
Alexandre Drouin
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Decision making based on statistical association alone can be a dangerous endeavor due to non-causal associations. Ideally, one would rely o… (voir plus)n causal relationships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter-ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures
In Search of Robust Measures of Generalization
Gintare Karolina Dziugaite
Alexandre Drouin
Brady Neal
Nitarshan Rajkumar
Ethan Caballero
Linbo Wang
Ioannis Mitliagkas
Daniel M. Roy
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now tra… (voir plus)ins networks to achieve small training error also leads to small error on held-out data from the same population. It is widely appreciated that some worst-case theories -- such as those based on the VC dimension of the class of predictors induced by modern neural network architectures -- are unable to explain empirical performance. A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk. When evaluated empirically, however, most of these bounds are numerically vacuous. Focusing on generalization bounds, this work addresses the question of how to evaluate such bounds empirically. Jiang et al. (2020) recently described a large-scale empirical study aimed at uncovering potential causal relationships between bounds/measures and generalization. Building on their study, we highlight where their proposed methods can obscure failures and successes of generalization measures in explaining generalization. We argue that generalization measures should instead be evaluated within the framework of distributional robustness.
arxiv.org
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste
Pau Rodr'iguez
Frédéric Branchaud-charron
Parmida Atighehchian
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Matt P. Craddock
Laurent Charlin
David Vazquez
arxiv.org