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- Yaroslav Ganin, Evgeniya Ustinova, +5 authors Victor S. Lempitsky
- Domain Adaptation in Computer Vision Applications
- 2016

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 directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot… (More)

We consider a Perceptron with Ni input units, one output and a yet unspecified number of hidden units. This Perceptron must be able to learn a given but arbitrary set of input-output examples. By sequential learning we mean that groups of patterns, pertaining to the same class, are sequentially separated from the rest by successively adding hidden units… (More)

We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds are obtained as particular cases. We also propose different learning algorithms for finding linear classifiers that minimize these bounds. These learning algorithms are generally competitive with both AdaBoost and the SVM.

In this paper, we We a close look at the problem of l&ing simple neural concepts under the uniform diseibution of examples By simple neural concepts we mean concepts that can be represented as simple combinations of perceptrons (halfspaces). One such class of concepts is the class of halfspace intersections. By formalizing the problem of learning halfspace… (More)

- Mario Marchand, John Shawe-Taylor
- Journal of Machine Learning Research
- 2002

We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a trade-off between accuracy and complexity. The result is a generalpurpose learning machine, suitable for practical learning tasks, that we call the set… (More)

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 distributions. Our algorithm is directly inspired by theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on a data… (More)

We propose to construct a weighted majority vote on a set of basis functions by minimizing a risk bound (called the C-bound) that depends on the first two moments of the margin of the Q-convex combination realized on the data. This bound minimization algorithm turns out to be a quadratic program that can be efficiently solved. A first version of the… (More)

- Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
- Journal of Machine Learning Research
- 2015

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 takes into account the average quality of the voters and their average disagreement. We also propose an extensive PAC-Bayesian analysis that shows how the C-bound can be estimated… (More)

- Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil
- BMC Bioinformatics
- 2012

The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also… (More)

- Sébastien Boisvert, Mario Marchand, François Laviolette, Jacques Corbeil
- Retrovirology
- 2008

Human immunodeficiency virus type 1 (HIV-1) infects cells by means of ligand-receptor interactions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor, either CXCR4 or CCR5, to enter a target cell. HIV-1 is characterized by high sequence variability. Nonetheless, within this extensive variability, certain features must be… (More)