
How to Certify Machine Learning Based Safetycritical Systems? A Systematic Literature Review
Context: Machine Learning (ML) has been at the heart of many innovations...
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Implicit Variational Inference: the Parameter and the Predictor Space
Having access to accurate confidence levels along with the predictions a...
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Leveraging Subword Embeddings for Multinational Address Parsing
Address parsing consists of identifying the segments that make up an add...
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General Cops and Robbers Games with randomness
Cops and Robbers games have been studied for the last few decades in com...
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The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...
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Unsupervised Domain Adversarial SelfCalibration for Electromyographicbased Gesture Recognition
Surface electromyography (sEMG) provides an intuitive and noninvasive i...
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Virtual Reality to Study the Gap Between Offline and RealTime EMGbased Gesture Recognition
Within sEMGbased gesture recognition, a chasm exists in the literature ...
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Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
The research in myoelectric control systems primarily focuses on extract...
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Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for co...
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Dichotomize and Generalize: PACBayesian Binary Activated Deep Neural Networks
We present a comprehensive study of multilayer neural networks with bina...
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Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning
In recent years, the use of deep learning algorithms has become increasi...
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Maximum Margin Interval Trees
Learning a regression function using censored or intervalvalued output ...
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PACBayes and Domain Adaptation
We provide two main contributions in PACBayesian theory for domain adap...
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A New PACBayesian Perspective on Domain Adaptation
We study the issue of PACBayesian domain adaptation: We want to learn, ...
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DomainAdversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptatio...
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Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
The Set Covering Machine (SCM) is a greedy learning algorithm that produ...
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Risk Bounds for the Majority Vote: From a PACBayesian Analysis to a Learning Algorithm
We propose an extensive analysis of the behavior of majority votes in bi...
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PACBayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
In this paper, we provide two main contributions in PACBayesian theory ...
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An Improvement to the Domain Adaptation Bound in a PACBayesian context
This paper provides a theoretical analysis of domain adaptation based on...
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On Generalizing the CBound to the Multiclass and Multilabel Settings
The Cbound, introduced in Lacasse et al., gives a tight upper bound on ...
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DomainAdversarial Neural Networks
We introduce a new representation learning algorithm suited to the conte...
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Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
The increased affordability of whole genome sequencing has motivated its...
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On the Generalization of the CBound to Structured Output Ensemble Methods
This paper generalizes an important result from the PACBayesian literat...
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Sequential ModelBased Ensemble Optimization
One of the most tedious tasks in the application of machine learning is ...
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PACBayesian Learning and Domain Adaptation
In machine learning, Domain Adaptation (DA) arises when the distribution...
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Learning a peptideprotein binding affinity predictor with kernel ridge regression
We propose a specialized string kernel for small biomolecules, peptides...
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PACBayesian Inequalities for Martingales
We present a set of highprobability inequalities that control the conce...
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PACBayesian Analysis of the ExplorationExploitation Tradeoff
We develop a coherent framework for integrative simultaneous analysis of...
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PACBayesian Analysis of Martingales and Multiarmed Bandits
We present two alternative ways to apply PACBayesian analysis to sequen...
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François Laviolette
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Professor, Director of the Laval University Big Data research Centre at Université Laval