Publications

Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin (2024). TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. International Conference on Learning Representations (ICLR 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 (2024). WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?. International Conference on Machine Learning (ICML 2024).

Issam Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin (2023). Capture the Flag: Uncovering Data Insights with Large Language Models. NeurIPS 2023 Foundation Models for Decision Making Workshop.

Rim Assouel, Tom Marty, Massimo Caccia, Issam H Laradji, Alexandre Drouin, Sai Rajeswar, Hector Palacios, Quentin Cappart, David Vazquez, Nicolas Chapados, Maxime Gasse, Alexandre Lacoste (2023). The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study. NeurIPS 2023 Foundation Models for Decision Making Workshop.

Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Biloš, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish (2023). Lag-llama: Towards foundation models for time series forecasting. Preprint.

Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados (2023). Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. 40th International Conference on Machine Learning (ICML 2023).

Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin (2023). Invariant Causal Set Covering Machines. ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability.

Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin (2023). Causal Discovery with Language Models as Imperfect Experts. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.

Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio (2023). Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.

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 (2023). GEO-Bench: Toward Foundation Models for Earth Monitoring. Preprint.

Alexandre Drouin, Étienne Marcotte, Nicolas Chapados (2022). TACTiS: Transformer-Attentional Copulas for Time Series. 39th International Conference on Machine Learning (ICML 2022) – Spotlight Presentation.

Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin (2022). Typing Assumptions Improve Identification in Causal Discovery. 1st Conference on Causal Learning and Reasoning (CLeaR 2022) – Oral Presentation.

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 (2021). Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark. NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning.

Xiang Zhang, Alexandre Drouin, Raymond Li (2021). byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings. Preprint.

Gintare Karolina Dziugaite*, Alexandre Drouin*, Brady Neal, Nitarshan Rajkumar, Ethan Victor Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy (2020). In search of robust measures of generalization. Neural Information Processing Systems (NeurIPS 2020).

Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez (2020). Synbols: Probing Learning Algorithms with Synthetic Datasets. Neural Information Processing Systems (NeurIPS 2020).

Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin (2020). Differentiable Causal Discovery from Interventional Data. Neural Information Processing Systems (NeurIPS 2020) – Spotlight Presentation.

Philippe Brouillard, Alexandre Drouin, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien (2020). Gradient-based Neural DAG Learning with Interventions. Causal Learning for Decision Making workshop, ICLR 2020.

Pau Rodríguez, Issam Laradji, Alexandre Drouin, Alexandre Lacoste (2020). Embedding Propagation: Smoother Manifold for Few-Shot Classification. European Conference on Computer Vision (ECCV 2020).

Francis Brochu, Pier-Luc Plante, Alexandre Drouin, Dominic Gagnon, Dave Richard, Francine Durocher, Caroline Diorio, Mario Marchand, Jacques Corbeil, François Laviolette (2019). Mass spectra alignment using virtual lock-masses. Scientific reports.

Alexandre Drouin, Gaël Letarte, Frédéric Raymond, Mario Marchand, Jacques Corbeil, François Laviolette (2019). Interpretable genotype-to-phenotype classifiers with performance guarantees. Scientific reports.

Alexandre Drouin (2019). Inferring phenotypes from genotypes with machine learning: an application to the global problem of antibiotic resistance. PhD Thesis, Université Laval.

Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin (2019). Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Alexandre Drouin, Toby Hocking, François Laviolette (2017). Maximum margin interval trees. Neural Information Processing Systems (NIPS 2017).

Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, Mario Marchand, Michael Tyers, Vivian G Loo, Anne-Marie Bourgault, François Laviolette, Jacques Corbeil (2016). Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genomics.

Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette (2016). Large scale modeling of antimicrobial resistance with interpretable classifiers. Machine Learning for Health Workshop, NIPS.

Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil (2015). Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. Greed is Great Workshop, ICML.

Alexandre Drouin, Sébastien Giguere, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil (2014). Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. Machine Learning in Computational Biology Workshop, NIPS.

Sébastien Giguère, Alexandre Drouin, Alexandre Lacoste, Mario Marchand, Jacques Corbeil, François Laviolette (2013). MHC-NP: predicting peptides naturally processed by the MHC. Journal of immunological methods.

Sébastien Giguere, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil (2013). Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC bioinformatics.

Maxime Latulippe, Alexandre Drouin, Philippe Giguère, François Laviolette (2013). Accelerated Robust Point Cloud Registration in Natural Environments through Positive and Unlabeled Learning. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.