François Laviolette

Associate Academic Member
François Laviolette
Full Professor, Université Laval
François Laviolette

François Laviolette is a full professor in the Computer Science and Software Engineering Department at Université Laval. His research focuses on artificial intelligence, especially machine learning. A leader in PAC-Bayesian theory, which helps to better understand machine learning algorithms and to design new ones, he is interested, among others, in those that solve genomics-related learning problems, proteomics and drug discovery. He is also interested in making artificial intelligences interpretable in order to better integrate systems where humans are in the decision loop. He is the Director of the Big Data Research Center at Laval University, which brings together more than 50 professors.

Publications

2020-10

Implicit Variational Inference: the Parameter and the Predictor Space
Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais and François Laviolette
arXiv preprint arXiv:2010.12995
(2020-10-24)
arxiv.orgPDF

2020-09

Fast greedy $$\mathcal {C}$$ C -bound minimization with guarantees
Baptiste Bauvin, Cécile Capponi, Jean-Francis Roy and François Laviolette
Machine Learning
(2020-09-23)
link.springer.com

2020-06

Leveraging Subword Embeddings for Multinational Address Parsing
Marouane Yassine, David Beauchemin, François Laviolette and Luc Lamontagne
arXiv preprint arXiv:2006.16152
(2020-06-29)
ui.adsabs.harvard.edu
Machine learning analysis identifies genes differentiating triple negative breast cancers.
Charu Kothari, Mazid Abiodoun Osseni, Lynda Agbo, Geneviève Ouellette, Maxime Déraspe, François Laviolette, Jacques Corbeil, Jean-Philippe Lambert, Caroline Diorio and Francine Durocher
Scientific Reports
(2020-06-26)
www.nature.com

2020-04

General Cops and Robbers Games with randomness.
Frédéric Simard, Josée Desharnais and François Laviolette
arXiv preprint arXiv:2004.11503
(2020-04-24)
ui.adsabs.harvard.eduPDF
Fast protein database as a service with kAAmer
Maxime Deraspe, Sebastien Boisvert, Francois Laviolette, Paul H Roy and Jacques Corbeil
bioRxiv
(2020-04-02)
www.biorxiv.orgPDF
Machine learning-based models of sawmills for better wood allocation planning
Michael Morin, Jonathan Gaudreault, Edith Brotherton, Frédérik Paradis, Amélie Rolland, Jean Wery and François Laviolette
International Journal of Production Economics
(2020-04-01)
www.sciencedirect.com

2020-03

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.
Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin and Erik Scheme
Frontiers in Bioengineering and Biotechnology
(2020-03-03)
www.frontiersin.org
Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.
Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin and Erik Scheme
Frontiers in Bioengineering and Biotechnology
(2020-03-03)
www.frontiersin.org[Also on arXiv preprint arXiv:1912.00283 (2019-11-30)]

2020-02

PAC-Bayes and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette and Emilie Morvant
Neurocomputing
(2020-02-28)
www.sciencedirect.comPDF

2020-01

The Indian Chefs Process.
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-draa, Marcel van Gerven and François Laviolette
arXiv preprint arXiv:2001.10657
(2020-01-29)
dblp.uni-trier.dePDF
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition.
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik Scheme, François Laviolette and Benoit Gosselin
IEEE Access
(2020-01-01)
ui.adsabs.harvard.eduPDF
The Indian Chefs Process.
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-draa, Marcel van Gerven and François Laviolette
UAI 2020
(2020-01-01)
www.auai.org

2019-12

Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition.
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik Scheme, François Laviolette and Benoit Gosselin
arXiv preprint arXiv:1912.11037
(2019-12-21)
ui.adsabs.harvard.eduPDF
Virtual Reality to Study the Gap Between Offline and Real-Time EMG-based Gesture Recognition
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik Scheme, François Laviolette and Benoit Gosselin
arXiv preprint arXiv:1912.09380
(2019-12-16)
ui.adsabs.harvard.eduPDF
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte, Pascal Germain, Benjamin Guedj and Francois Laviolette
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF

2019-09

MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING
Prudencio Tossou, Basile Dura, Daniel Cohen, Mario Marchand, François Laviolette and Alexandre Lacoste
(venue unknown)
(2019-09-25)
openreview.netPDF
Finite Approximation of LMPs for Exact Verification of Reachability Properties.
Gildas Kouko, Josée Desharnais and François Laviolette
QEST 2019
(2019-09-10)
link.springer.com

Publications collected and formatted using Paperoni

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