Portrait of Alexandre Drouin

Alexandre Drouin

Associate Industry Member
Adjunct professor, Université Laval, Department of Electrical Engineering and Computer Engineering
Research Scientist, ServiceNow
Research Topics
Causality
Computational Biology
Deep Learning
LLM Agent
Time Series Forecasting
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Biography

Alexandre Drouin is a research scientist at ServiceNow Research in Montréal, and an adjunct professor of computer science at Université Laval. He also leads ServiceNow’s Human Decision Support research program, which explores the use of machine learning for decision-making in complex dynamic environments.

Droiun’s main research interest is causal decision-making under uncertainty, where the goal is to answer questions of causal nature (interventions, counterfactual), while accounting for sources of uncertainty, such as ambiguity in causal structures and unmeasured variables. He is also interested in probabilistic time series forecasting and its use in foreseeing the long-term effect of actions. His PhD in computer science from Université Laval was on machine learning algorithms for biomarker discovery in large genomic datasets and their application to the global problem of antibiotic resistance.

Current Students

Arjun Ashok
PhD - Université de Montréal
Principal supervisor :
Irina Rish
Website
Github
Google Scholar
Léo Boisvert
PhD - Polytechnique Montréal
Co-supervisor :
Quentin Cappart
Philippe Brouillard
PhD - Université de Montréal
Principal supervisor :
Dhanya Sridhar
Website
Github
Google Scholar
Oscar Clivio
Research Intern - Université de Montréal
Principal supervisor :
Ioannis Mitliagkas
Website
Github
Google Scholar
Thibaud Godon
PhD - Université Laval
Principal supervisor :
Pascal Germain
Github
Google Scholar
Navita Goyal
Research Intern - Université de Montréal
Principal supervisor :
Dhanya Sridhar
Website
Github
Google Scholar

Publications

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… (see more)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 (published)
proceedings.mlr.press
arxiv.org
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites
Faizy Ahsan
Alexandre Drouin
François Laviolette
Doina Precup
Mathieu Blanchette
The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant met… (see more)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) (published)
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… (see more)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… (see more)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
2020-01-01
(published)
www.semanticscholar.org
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… (see more)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