Portrait de Myriam Lizotte

Myriam Lizotte

Doctorat - UdeM
Superviseur⋅e principal⋅e
Guy Wolf
Sujets de recherche
Apprentissage multimodal
Biologie computationnelle
Exploration des données

Publications

Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…
2025-02-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…
2025-02-18
ArXiv (prépublication)
doi.org
arxiv.org
F66. FROM GENE TO COGNITION: MAPPING THE EFFECTS OF GENOMIC DELETIONS AND DUPLICATIONS ON COGNITIVE ABILITY
Sayeh Kazem
Kuldeep Kumar
Guillaume Huguet
Myriam Lizotte
Thomas Renne
Jakub Kopal
Stefan Horoi
Martineau Jean-Louis
Zohra Saci
Laura Almasy
David C. Glahn
Guy Wolf
Guillaume Dumas
Sébastien Jacquemont
2023-10-01
European Neuropsychopharmacology (publié)
doi.org
Manifold Alignment with Label Information
Andres F. Duque Correa
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integrat… (voir plus)ion of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI belongs to a middle ground between the more commonly addressed semi-supervised manifold alignment, where some known correspondences between the two domains are assumed to be known beforehand, and the purely unsupervised case, where no information linking both domains is available. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. MALI recovers a pairing and a common representation that reveals related samples in both domains. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.
2023-05-21
SampTA/2023/Conference (publié)
doi.org
openreview.net