246 citations · 543 across the 21 of their papers we have counts for
21 papers
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation
Qi Chen, Mario Marchand
We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. Concretely, we study joint distr…
Generalization Properties of Decision Trees on Real-valued and Categorical Features
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
We revisit binary decision trees from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to th…
Fool SHAP with Stealthily Biased Sampling
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara +2
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies h…
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
We significantly improve the generalization bounds for VC classes by using two main ideas. First, we consider the hypergeometric tail inversion to obtain a very tight non-uniform d…
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
Gabriel Laberge, Yann Pequignot, Alexandre Mathieu +2
Post-hoc global/local feature attribution methods are progressively being employed to understand the decisions of complex machine learning models. Yet, because of limited amounts o…
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
Qi Chen, Changjian Shui, Mario Marchand
We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms. Concretely, our analysis proposes a generic understanding of both the c…
Decision trees as partitioning machines to characterize their generalization properties
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
Decision trees are popular machine learning models that are simple to build and easy to interpret. Even though algorithms to learn decision trees date back to almost 50 years, key…
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…
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…
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…
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…
Improved design and screening of high bioactivity peptides for drug discovery
Sébastien Giguère, François Laviolette, Mario Marchand +4
The discovery of peptides having high biological activity is very challenging mainly because there is an enormous diversity of compounds and only a minority have the desired proper…
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Sébastien Giguère, Mario Marchand, François Laviolette +2
We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino ac…
A Note on Improved Loss Bounds for Multiple Kernel Learning
Zakria Hussain, John Shawe-Taylor, Mario Marchand
In this paper, we correct an upper bound, presented in~\cite{hs-11}, on the generalisation error of classifiers learned through multiple kernel learning. The bound in~\cite{hs-11}…
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Mohak Shah, Mario Marchand, Jacques Corbeil
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance…