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# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## Pages

About

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## Posts

** Published:**

## Correlation coefficient

## portfolio

Short description of portfolio item number 1

Short description of portfolio item number 2

## publications

## Learning the Intensity of Time Events with Change-Points

**M. Z. Alaya, S. Gaïffas, A. Guilloux**

*IEEE Transactions on Information Theory*, 2015

We consider the problem of learning the inhomogeneous intensity of a counting process, under a sparse segmentation assumption. We introduce a weighted total-variation penalization, using data-driven weights that correctly scale the penalization along the observation interval. We prove that this leads to a sharp tuning of the convex relaxation of the segmentation prior, by stating oracle inequalities with fast rates of convergence, and consistency for change-points detection. **Read more**

## High-Dimensional Time-Varying Aalen and Cox Models

**M. Z. Alaya, T. Allart, A. Guilloux, S. Lemler**

*in revision to Journal of Nonparametric Statitics*, 2017

We consider the problem of estimating the intensity of a counting process in high-dimensional time-varying Aalen and Cox models. We introduce a covariate-specific weighted total-variation penalization, using data-driven weights that correctly scale the penalization along the observation interval. **Read more**

## Binarsity: a Penalization for One-Hot Encoded Features in Linear Supervised Learning

**M. Z. Alaya, S. Bussy, S. Gaïffas, A. Guilloux**

*Journal of Machine Learning Research*, 2019

This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-known trick of one-hot encoding of continuous features with a new penalization called *binarsity*. In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses totalvariation regularization together with an extra linear constraint. **Read more**

## Collective Matrix Completion

**M. Z. Alaya, O. Klopp**

*Journal of Machine Learning Research*, 2019

Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be, for example, a rating matrix in recommendation system. However, in practical situations, data is often obtained from multiple sources which results in a collection of matrices rather than a single one. In this work, we consider the problem of collective matrix completion with multiple and heterogeneous matrices, which can be count, binary, continuous, etc. **Read more**

## Screening Sinkhorn Algorithm for Regularized Optimal Transport

**M. Z. Alaya, M. Bérar, G. Gasso, A. Rakotomamonjy**

*NeurIPS*, 2019

We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures. After identifying neglectable components of the dual solution of the regularized Sinkhorn problem, we propose to screen those components by directly setting them at that value before entering the Sinkhorn problem. This allows us to solve a smaller Sinkhorn problem while ensuring approximation with provable guarantees. **Read more**

## Non-Aligned Distribution Distance using Metric Measure Embedding and Optimal Transport

**M. Z. Alaya, M. Bérar, G. Gasso, A. Rakotomamonjy**

*arXiv*, 2020

We propose a novel approach for comparing distributions whose supports do not necessarily lie on the same metric space. Unlike Gromov-Wasserstein (GW) distance that compares pairwise distance of elements from each distribution, we consider a method that embeds the metric measure spaces in a common Euclidean space and computes an optimal transport (OT) on the embedded distributions. This leads to what we call a *sub-embedding robust Wasserstein* (SERW). Under some conditions, SERW is a distance that considers an OT distance of the (low-distorted) embedded distributions using a common metric. **Read more**

## Binacox: Automatic Cut-Points Detection in High-Dimensional Cox Model, with Applications to Genetic Data

**S. Bussy, M. Z. Alaya, A. Guilloux, A.-S. Jannot**

*in revision to Biometrics*, 2020

We introduce the *binacox*, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the *binarsity* penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. **Read more**

## Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning

**L. Chapel, M. Z. Alaya, G. Gasso**

*NeurIPS*, 2020

Optimal Transport (OT) framework allows defining similarity between probability distributions and provides metrics such as the Wasserstein and Gromov-Wasserstein discrepancies. Classical OT problem seeks a transportation map that preserves the total mass, requiring the mass of the source and target distributions to be the same. This may be too restrictive in certain applications such as color or shape matching, since the distributions may have arbitrary masses or that only a fraction of the total mass has to be transported. Several algorithms have been devised for computing unbalanced Wasserstein metrics but when it comes with the Gromov-Wasserstein problem, no partial formulation is available yet. **Read more**

## talks

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## teaching

## Autumn 2012, Probability and Statistics Autumn 2012, Probability and Statistics

Master 1 Mechanical Engineering, *University Pierre and Marice Curie, Department of Engineering*, 2012

## Spring 2013, Algebra and Geometry Spring 2013, Algebra and Geometry

Licence 2 Mathematics, *University Pierre and Marice Curie, Departement of Mathematics*, 2013

## Autumn 2013, Linear Models II Autumn 2013, Linear Models II

Master 1 Actuary, *University Pierre and Marice Curie, Department of Statistics*, 2013

## Spring 2014, Algebra and Geometry Spring 2014, Algebra and Geometry

Licence 2 Mathematics, *University Pierre and Marice Curie, Departement of Mathematics*, 2014

## Autumn 2015, Times Series Autumn 2015, Times Series

Master 1 Actuary, *University Pierre and Marice Curie, Department of Statistics*, 2015

## Spring 2016, Mathematical Statistics Spring 2016, Mathematical Statistics

Licence 3 Mathematics, *University Pierre and Marie Curie, Department of Statistics*, 2016

## Autumn 2016, Real Analysis and C2I Certificate Autumn 2016, Real Analysis and C2I Certificate

Licence 1 and 2 Mathematics, *University Paris Nanterre, Department of Mathematics*, 2016

## Spring 2017, Statistics Spring 2017, Statistics

Licence 1 and 2 Psychology, *University Paris Nanterre, Department of Psychology*, 2017

## Autumn 2020, MT23 Autumn 2020, MT23

TC01, TC02, TC03, TC04, *UTC, Department of Computer Sciences*, 2020

Vous trouvez ici les corrections des exercices laissées pour les compléter à la maison.