Alexandre Drouin
Research Lead
Human Decision Support
Alexandre Drouin is a Research Scientist and the Research Lead of the Human Decision Support Program. He joined ServiceNow Research from Element AI, which he integrated in 2017. He holds a Ph.D. in Computer Science from Université Laval, where he was supervised by François Laviolette. In addition to his position at ServiceNow Research, he is an Adjunct Professor in the Department of Computer Science and Software Engineering at Université Laval and is a member of the scientific committee of Brief.Science. Alexandre’s research interests include algorithms for causal discovery/inference from observational data, practical applications of causal inference, learning robust models from heterogeneous data, and time series forecasting.
Interests
- Causality
- Time Series Forecasting
- Decission Making
Publications
Invariant Causal Set Covering Machines.
Workshop on Spurious Correlations, Invariance, and Stability (ICML),
2023.
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation.
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),
2023.
Causal Discovery with Language Models as Imperfect Experts.
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),
2023.
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts.
International Conference on Machine Learning (ICML),
2023.
TACTiS: Transformer-attentional copulas for time series.
Montreal AI Symposium (MAIS),
2022.
TACTiS: Transformer-Attentional Copulas for Time Series.
International Conference on Machine Learning (ICML),
2022.
Deconfounding Dynamic Treatment Regimes.
SSC,
2022.
Typing assumptions improve identification in causal discovery - theory and algorithms.
Causal Learning and Reasoning (CLeaR),
2022.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark.
Workshop at the Neural Information Processing Systems (NeurIPS),
2021.
Typing assumptions improve identification in causal discovery - Report and comments on future directions.
Workshop at the Neural Information Processing Systems (NeurIPS),
2021.
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data.
Intelligent Systems for Molecular Biology and European Conference on Computational Biology,
2021.
Typing assumptions improve identification in causal discovery.
Workshop at the International Conference on Machine Learning (ICML),
2021.
Differentiable Causal Discovery from Interventional Data.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
In search of robust measures of generalization.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Synbols: Probing Learning Algorithms with Synthetic Datasets.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Embedding Propagation: Smoother Manifold for Few-Shot Classification.
European Conference on Computer Vision (ECCV),
2020.
Gradient-Based Neural DAG Learning with Interventions.
Workshop at the International Conference on Learning Representations (ICLR),
2020.
Phylogenetic Manifold Regularization: a semi-supervised approach to predict transcription factor binding sites.
International Conference on Bioinformatics and Biomedicine (BIBM),
2020.
Mass spectra alignment using virtual lock-masses.
Nature Scientific Reports,
2019.
Interpretable genotype-to-phenotype classifiers with performance guarantees.
Nature Scientific Reports,
2018.
Synbols: Probing Learning Algorithms with Synthetic Datasets.
Montreal AI Symposium (MAIS),
2018.
Deep learning for electromyographic hand gesture signal classification using transfer learning.
Transactions on Neural Systems and Rehabilitation Engineering,
2018.