In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set...
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September 21, 2014 (v1)Conference paperUploaded on: March 25, 2023
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December 13, 2015 (v1)Conference paper
As the number of samples and dimensionality of optimization problems related to statistics an machine learning explode, block coordinate descent algorithms have gained popularity since they reduce the original problem to several smaller ones. Coordinates to be optimized are usually selected randomly according to a given probability...
Uploaded on: February 28, 2023 -
October 20, 2015 (v1)Report
The objectives of this technical report is to provide additional results on the generalized conditional gradient methods introduced by Bredies et al. [BLM05]. Indeed , when the objective function is smooth, we provide a novel certificate of optimality and we show that the algorithm has a linear convergence rate. Applications of this algorithm...
Uploaded on: March 25, 2023 -
2016 (v1)Journal article
We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation. The algorithm consists in obtaining a descent...
Uploaded on: March 25, 2023 -
July 29, 2016 (v1)Book section
This chapter introduces statistical learning and its applications to brain-computer interfaces (BCIs). It presents the general principles of supervised learning and discusses the difficulties raised by its implementation, with a particular focus on aspects related to selecting sensors and multisubject learning. The chapter also describes how a...
Uploaded on: December 4, 2022 -
May 4, 2014 (v1)Conference paper
The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the...
Uploaded on: March 25, 2023 -
2012 (v1)Journal article
We propose a principled framework for learning with infinitely many features, situations that are usually induced by continuously parametrized feature extraction methods. Such cases occur for instance when considering Gabor-based features in computer vision problems or when dealing with Fourier features for kernel approximations. We cast the...
Uploaded on: December 3, 2022 -
September 18, 2017 (v1)Conference paper
Brain Computer Interfaces suffer from considerable cross-session and cross-subject variability, which makes it hard for classification methods to generalize. We introduce a transfer learning method based on regularized discrete optimal transport with class labels in the interest of enhancing the generalization capacity of state-of-the-art...
Uploaded on: March 25, 2023 -
June 22, 2016 (v1)Publication
Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its...
Uploaded on: February 28, 2023 -
2016 (v1)Journal article
Domain adaptation is one of the most chal- lenging tasks of modern data analytics. If the adapta- tion is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant...
Uploaded on: February 28, 2023 -
December 2014 (v1)Conference paper
We propose a method based on optimal transport for empirical distributions with Laplacian regularization (LOT). Laplacian regularization is a graph-based regu-larization that can encode neighborhood similarity between samples either on the final position of the transported samples or on their displacement. In both cases, LOT is expressed as a...
Uploaded on: March 25, 2023 -
December 2018 (v1)Journal article
Wasserstein Discriminant Analysis (WDA) is a new supervised method that canimprove classification of high-dimensional data by computing a suitable linearmap onto a lower dimensional subspace. Following the blueprint of classical Lin-ear Discriminant Analysis (LDA), WDA selects the projection matrix that maxi-mizes the ratio of two...
Uploaded on: February 28, 2023 -
2022 (v1)Conference paper
The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important. A key challenge is to design an approach that overcomes the covariate and target shift both among the sources, and between the source and target domains. In this paper, we address this...
Uploaded on: December 4, 2022 -
December 2017 (v1)Conference paper
This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function f in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a non-linear transformation between the...
Uploaded on: February 28, 2023 -
2022 (v1)Conference paper
The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important. A key challenge is to design an approach that overcomes the covariate and target shift both among the sources, and between the source and target domains. In this paper, we address this...
Uploaded on: February 22, 2023 -
December 2014 (v1)Conference paper
Domain adaptation from one data space (or domain) to the other is one of the most challenging tasks of modern data analytics. If the adaptation is done cor-rectly, models built on a specific data space become able to process data depicting the same semantic concepts (the classes), but observed by another observation system with its own...
Uploaded on: March 25, 2023 -
June 22, 2020 (v1)Publication
The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important. A key challenge is to design an approach that overcomes the covariate and target shift both among the sources, and between the source and target domains. In this paper, we address this...
Uploaded on: December 4, 2022 -
July 2015 (v1)Conference paper
—Re-using models trained on a specific image acquisition to classify landcover in another image is no easy task. Illumination effects, specific angular configurations, abrupt and simple seasonal changes make that the spectra observed, even though representing the same kind of surface, drift in a way that prevents a non-adapted model to perform...
Uploaded on: February 28, 2023 -
April 14, 2014 (v1)Journal article
This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task...
Uploaded on: December 2, 2022 -
April 14, 2014 (v1)Journal article
This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task...
Uploaded on: October 11, 2023 -
November 7, 2018 (v1)Publication
This paper presents a dissimilarity-based discriminative framework for learning from data coming in the form of probability distributions. Departing from the use of positive kernel-based methods, we build upon embeddings based on dissimilarities tailored for distribution. We enable this by extending \citet{balcan2008theory}'s theory ...
Uploaded on: December 4, 2022 -
October 15, 2020 (v1)Publication
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the class-conditional probabilities. For this...
Uploaded on: December 4, 2022 -
April 1, 2018 (v1)Journal article
Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been...
Uploaded on: December 4, 2022