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06/23/2021
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
We investigate a stochastic counterpart of majority votes over finite en...
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04/28/2021
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
In the PAC-Bayesian literature, the C-Bound refers to an insightful rela...
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02/19/2021
A PAC-Bayes Analysis of Adversarial Robustness
We propose the first general PAC-Bayesian generalization bounds for adve...
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02/17/2021
A General Framework for the Derandomization of PAC-Bayesian Bounds
PAC-Bayesian bounds are known to be tight and informative when studying ...
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04/24/2020
A survey on domain adaptation theory
All famous machine learning algorithms that correspond to both supervise...
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09/04/2019
Metric Learning from Imbalanced Data
A key element of any machine learning algorithm is the use of a function...
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06/14/2019
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
We propose a Gradient Boosting algorithm for learning an ensemble of ker...
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10/30/2018
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF...
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08/17/2018
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
In this paper we propose a boosting based multiview learning algorithm, ...
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05/25/2018
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
We tackle the issue of classifier combinations when observations have mu...
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07/17/2017
PAC-Bayes and Domain Adaptation
We provide two main contributions in PAC-Bayesian theory for domain adap...
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06/23/2016
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
We study a two-level multiview learning with more than two views under t...
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06/15/2015
A New PAC-Bayesian Perspective on Domain Adaptation
We study the issue of PAC-Bayesian domain adaptation: We want to learn, ...
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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 ...
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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...
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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 ...
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10/01/2014
Domain adaptation of weighted majority votes via perturbed variation-based self-labeling
In machine learning, the domain adaptation problem arrives when the test...
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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...
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04/30/2014
Majority Vote of Diverse Classifiers for Late Fusion
In the past few years, a lot of attention has been devoted to multimedia...
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11/19/2013
Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer
We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives ...
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12/11/2012
PAC-Bayesian Learning and Domain Adaptation
In machine learning, Domain Adaptation (DA) arises when the distribution...
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07/04/2012
PAC-Bayesian Majority Vote for Late Classifier Fusion
A lot of attention has been devoted to multimedia indexing over the past...
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02/28/2012
