1 code implementation • 19 Feb 2024 • Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework.
1 code implementation • NeurIPS 2021 • Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties.
1 code implementation • 28 Apr 2021 • Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant
In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss) and the first two moments of its margin (i. e., the expected margin and the voters' diversity).
1 code implementation • NeurIPS 2021 • Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant
We propose the first general PAC-Bayesian generalization bounds for adversarial robustness, that estimate, at test time, how much a model will be invariant to imperceptible perturbations in the input.
1 code implementation • 17 Feb 2021 • Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant
PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers.
no code implementations • 24 Apr 2020 • Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani
Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation.
no code implementations • 4 Sep 2019 • Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points.
no code implementations • 14 Jun 2019 • Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter.
1 code implementation • 30 Oct 2018 • Gaël Letarte, Emilie Morvant, Pascal Germain
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory.
2 code implementations • 17 Aug 2018 • Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
1 code implementation • 25 May 2018 • Anil Goyal, Emilie Morvant, Massih-Reza Amini
We tackle the issue of classifier combinations when observations have multiple views.
no code implementations • 17 Jul 2017 • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk.
no code implementations • 23 Jun 2016 • Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework.
1 code implementation • 15 Jun 2015 • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
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.
no code implementations • 24 Mar 2015 • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
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 majority vote on a different target distribution.
no code implementations • 13 Jan 2015 • Francois Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier.
no code implementations • 13 Jan 2015 • Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory.
no code implementations • NeurIPS 2014 • Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.
no code implementations • 1 Oct 2014 • Emilie Morvant
In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions.
no code implementations • 6 Aug 2014 • François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs.
no code implementations • 30 Apr 2014 • Emilie Morvant, Amaury Habrard, Stéphane Ayache
Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse.
no code implementations • 14 Apr 2014 • Vladimir Kolmogorov, Christoph Lampert, Emilie Morvant, Rustem Takhanov
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (\"OAGM) will be held at IST Austria, on May 22-23, 2014.
no code implementations • 19 Nov 2013 • Emilie Morvant
In non-DA supervised setting, a theoretical bound - the C-bound - involves this disagreement and leads to a majority vote learning algorithm: MinCq.
no code implementations • 28 Feb 2012 • Emilie Morvant, Sokol Koço, Liva Ralaivola
In this work, we propose a PAC-Bayes bound for the generalization risk of the Gibbs classifier in the multi-class classification framework.