42 citations · 123 across the 19 of their papers we have counts for
20 papers
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu +2
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exis…
Causal Discovery with Language Models as Imperfect Experts
Stephanie Long, Alexandre Piché, Valentina Zantedeschi +2
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used t…
Invariant Causal Set Covering Machines
Thibaud Godon, Baptiste Bauvin, Pascal Germain +2
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
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 do…
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin +1
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.…
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin +3
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges l…