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09/26/2022
A simple way to learn metrics between attributed graphs
The choice of good distances and similarity measures between objects is ...
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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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10/30/2020
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
We present a novel multiview canonical correlation analysis model based ...
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10/05/2020
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
Most of existing deep learning models rely on excessive amounts of label...
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07/07/2020
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
We propose a novel algorithm for unsupervised graph representation learn...
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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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09/02/2019
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
In this paper, we address the challenging problem of learning from imbal...
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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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03/24/2018
Near-lossless Binarization of Word Embeddings
Is it possible to learn binary word embeddings of arbitrary size from th...
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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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05/24/2017
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where ...
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10/14/2016
Theoretical Analysis of Domain Adaptation with Optimal Transport
Domain adaptation (DA) is an important and emerging field of machine lea...
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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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09/18/2014
Subspace Alignment For Domain Adaptation
In this paper, we introduce a new domain adaptation (DA) algorithm where...
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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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06/28/2013
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity betw...
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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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09/05/2012
Robustness and Generalization for Metric Learning
Metric learning has attracted a lot of interest over the last decade, bu...
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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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06/27/2012