François Laviolette

research

∙ 12/07/2021

Multinational Address Parsing: A Zero-Shot Evaluation

Address parsing consists of identifying the segments that make up an add...

0 Marouane Yassine, et al. ∙

research

∙ 10/28/2021

Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations

Considering a probability distribution over parameters is known as an ef...

0 Louis Fortier-Dubois, et al. ∙

research

∙ 07/26/2021

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

Context: Machine Learning (ML) has been at the heart of many innovations...

0 Florian Tambon, et al. ∙

research

∙ 10/24/2020

Implicit Variational Inference: the Parameter and the Predictor Space

Having access to accurate confidence levels along with the predictions a...

0 Yann Pequignot, et al. ∙

research

∙ 06/29/2020

Leveraging Subword Embeddings for Multinational Address Parsing

Address parsing consists of identifying the segments that make up an add...

0 Marouane Yassine, et al. ∙

research

∙ 04/24/2020

General Cops and Robbers Games with randomness

Cops and Robbers games have been studied for the last few decades in com...

0 Frédéric Simard, et al. ∙

research

∙ 01/29/2020

The Indian Chefs Process

This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...

16 Patrick Dallaire, et al. ∙

research

∙ 12/21/2019

Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition

Surface electromyography (sEMG) provides an intuitive and non-invasive i...

0 Ulysse Côté-Allard, et al. ∙

research

∙ 12/16/2019

Virtual Reality to Study the Gap Between Offline and Real-Time EMG-based Gesture Recognition

Within sEMG-based gesture recognition, a chasm exists in the literature ...

24 Ulysse Côté-Allard, et al. ∙

research

∙ 11/30/2019

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features

The research in myoelectric control systems primarily focuses on extract...

0 Ulysse Côté-Allard, et al. ∙

research

∙ 05/28/2019

Adaptive Deep Kernel Learning

Deep kernel learning provides an elegant and principled framework for co...

0 Prudencio Tossou, et al. ∙

research

∙ 05/24/2019

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

We present a comprehensive study of multilayer neural networks with bina...

2 Gaël Letarte, et al. ∙

research

∙ 01/10/2018

Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning

In recent years, the use of deep learning algorithms has become increasi...

0 Ulysse Côté-Allard, et al. ∙

research

∙ 10/11/2017

Maximum Margin Interval Trees

Learning a regression function using censored or interval-valued output ...

0 Alexandre Drouin, et al. ∙

research

∙ 07/17/2017

PAC-Bayes and Domain Adaptation

We provide two main contributions in PAC-Bayesian theory for domain adap...

0 Pascal Germain, et al. ∙

research

∙ 06/15/2015

A New PAC-Bayesian Perspective on Domain Adaptation

We study the issue of PAC-Bayesian domain adaptation: We want to learn, ...

0 Pascal Germain, et al. ∙

research

∙ 05/28/2015

Domain-Adversarial Training of Neural Networks

We introduce a new representation learning approach for domain adaptatio...

0 Yaroslav Ganin, et al. ∙

research

∙ 05/22/2015

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

The Set Covering Machine (SCM) is a greedy learning algorithm that produ...

0 Alexandre Drouin, et al. ∙

research

∙ 03/28/2015

Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

We propose an extensive analysis of the behavior of majority votes in bi...

0 Pascal Germain, et al. ∙

research

∙ 03/24/2015

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

In this paper, we provide two main contributions in PAC-Bayesian theory ...

0 Pascal Germain, et al. ∙

research

∙ 01/13/2015

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context

This paper provides a theoretical analysis of domain adaptation based on...

0 Pascal Germain, et al. ∙

research

∙ 01/13/2015

On Generalizing the C-Bound to the Multiclass and Multi-label Settings

The C-bound, introduced in Lacasse et al., gives a tight upper bound on ...

0 François Laviolette, et al. ∙

research

∙ 12/15/2014

Domain-Adversarial Neural Networks

We introduce a new representation learning algorithm suited to the conte...

0 Hana Ajakan, et al. ∙

research

∙ 12/02/2014

Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine

The increased affordability of whole genome sequencing has motivated its...

0 Alexandre Drouin, et al. ∙

research

∙ 08/06/2014

On the Generalization of the C-Bound to Structured Output Ensemble Methods

This paper generalizes an important result from the PAC-Bayesian literat...

0 François Laviolette, et al. ∙

research

∙ 02/04/2014

Sequential Model-Based Ensemble Optimization

One of the most tedious tasks in the application of machine learning is ...

0 Alexandre Lacoste, et al. ∙

research

∙ 12/11/2012

PAC-Bayesian Learning and Domain Adaptation

In machine learning, Domain Adaptation (DA) arises when the distribution...

0 Pascal Germain, et al. ∙

research

∙ 07/31/2012

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides...

0 Sébastien Giguère, et al. ∙

research

∙ 10/31/2011

PAC-Bayesian Inequalities for Martingales

We present a set of high-probability inequalities that control the conce...

0 Yevgeny Seldin, et al. ∙

research

∙ 05/23/2011

PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off

We develop a coherent framework for integrative simultaneous analysis of...

0 Yevgeny Seldin, et al. ∙

research

∙ 05/12/2011

PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

We present two alternative ways to apply PAC-Bayesian analysis to sequen...

0 Yevgeny Seldin, et al. ∙

François Laviolette

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