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
Website
Google Scholar
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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

Publications

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (see more)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2023-10-02
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
openreview.net
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Emezue
Alexandre Drouin
Tristan Deleu
Stefan Bauer
Yoshua Bengio
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inf… (see more)erence. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision.
2023-07-11
ArXiv (preprint)
doi.org
arxiv.org
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Étienne Marcotte
Valentina Zantedeschi
Alexandre Drouin
Nicolas Chapados
Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expect… (see more)ation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the"region of reliability"of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (published)
doi.org
openreview.net
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Emezue
Alexandre Drouin
Tristan Deleu
Stefan Bauer
Yoshua Bengio
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net
Causal Discovery with Language Models as Imperfect Experts
Stephanie Long
Alexandre Piché
Valentina Zantedeschi
Tibor Schuster
Alexandre Drouin
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we ex… (see more)plore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net
Invariant Causal Set Covering Machines
Thibaud Godon
Baptiste Bauvin
Pascal Germain
Jacques Corbeil
Alexandre Drouin
2023-06-07
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
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-… (see more)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 (preprint)
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… (see more)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 (preprint)
doi.org
arxiv.org