no code implementations • 8 Apr 2024 • Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
2 code implementations • 12 Mar 2024 • Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, Nicolas Chapados, Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers.
no code implementations • 21 Dec 2023 • Issam Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
1 code implementation • 12 Oct 2023 • Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Hena Ghonia, Rishika Bhagwatkar, Arian Khorasani, Mohammad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, Marin Biloš, Sahil Garg, Anderson Schneider, Nicolas Chapados, Alexandre Drouin, Valentina Zantedeschi, Yuriy Nevmyvaka, Irina Rish
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
1 code implementation • 2 Oct 2023 • 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 forecasting, interpolation, and their combinations.
no code implementations • 11 Jul 2023 • Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio
Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
1 code implementation • 5 Jul 2023 • 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.
1 code implementation • 7 Jun 2023 • Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature.
1 code implementation • NeurIPS 2023 • 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 substantial increases in generalization to downstream tasks.
1 code implementation • 19 Apr 2023 • 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 expectation for the ground-truth distribution.
1 code implementation • 11 Aug 2022 • Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Elina Francovic-Fontaine, Alexandre Drouin, Jacques Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine.
1 code implementation • 7 Feb 2022 • 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.
no code implementations • 1 Dec 2021 • Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
1 code implementation • 22 Jul 2021 • Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sebastien Lachapelle, Alexandre Drouin
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
no code implementations • 24 Jun 2021 • Xiang Zhang, Alexandre Drouin, Raymond Li
This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings.
1 code implementation • NeurIPS 2020 • Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
4 code implementations • NeurIPS 2020 • Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
1 code implementation • NeurIPS 2020 • Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
1 code implementation • ECCV 2020 • Pau Rodríguez, Issam Laradji, Alexandre Drouin, Alexandre Lacoste
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
4 code implementations • 10 Jan 2018 • Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
1 code implementation • NeurIPS 2017 • Alexandre Drouin, Toby Dylan Hocking, François Laviolette
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine.
1 code implementation • 3 Dec 2016 • Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide.
no code implementations • 22 May 2015 • Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers.
no code implementations • 2 Dec 2014 • Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies.