246 citations · 863 across the 28 of their papers we have counts for
29 papers
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Florian Tambon, Gabriel Laberge, Le An +7
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aer…
Leveraging Subword Embeddings for Multinational Address Parsing
Marouane Yassine, David Beauchemin, François Laviolette +1
Address parsing consists of identifying the segments that make up an address such as a street name or a postal code. Because of its importance for tasks like record linkage, addres…
General Cops and Robbers Games with randomness
Frédéric Simard, Josée Desharnais, François Laviolette
Cops and Robbers games have been studied for the last few decades in computer science and mathematics. As in general pursuit evasion games, pursuers (cops) seek to capture evaders…
The Indian Chefs Process
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier +6
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes India…
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark +4
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance ov…
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark +4
Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a cla…
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark +3
The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features…
Adaptive Deep Kernel Learning
Prudencio Tossou, Basile Dura, Francois Laviolette +2
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods. By mea…
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin +5
In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of…
Maximum Margin Interval Trees
Alexandre Drouin, Toby Dylan Hocking, François Laviolette
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued pre…
Large scale modeling of antimicrobial resistance with interpretable classifiers
Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre +3
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome…
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a…
Efficient Learning of Ensembles with QuadBoost
Louis Fortier-Dubois, François Laviolette, Mario Marchand +2
We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set. We then propose a…
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe +3
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole gen…
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan +5
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is di…
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
Pascal Germain, Alexandre Lacasse, François Laviolette +2
We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that t…
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
Pascal Germain, Amaury Habrard, François Laviolette +1
In this paper, we provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majorit…
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
Pascal Germain, Amaury Habrard, Francois Laviolette +1
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germ…
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
Francois Laviolette, Emilie Morvant, Liva Ralaivola +1
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this…
On the String Kernel Pre-Image Problem with Applications in Drug Discovery
Sébastien Giguère, Amélie Rolland, François Laviolette +1
The pre-image problem has to be solved during inference by most structured output predictors. For string kernels, this problem corresponds to finding the string associated to a giv…
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich +4
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes…
Domain-Adversarial Neural Networks
Hana Ajakan, Pascal Germain, Hugo Larochelle +2
We introduce a new representation learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributio…
Sequential Model-Based Ensemble Optimization
Alexandre Lacoste, Hugo Larochelle, François Laviolette +1
One of the most tedious tasks in the application of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation…
PAC-Bayesian Learning and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data. It is well…