#### Filter Results:

- Full text PDF available (17)

#### Publication Year

2007

2017

- This year (1)
- Last 5 years (9)
- Last 10 years (19)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Sébastien Gadat, Laurent Younes
- Journal of Machine Learning Research
- 2007

We introduce a new model addressing feature selection from a large dictionary of variables that can be computed from a signal or an image. Features are extracted according to an efficiency criterion, on the basis of specified classification or recognition tasks. This is done by estimating a probability distribution P on the complete dictionary, which… (More)

- Jérémie Bigot, Sébastien Gadat, Jean-Michel Loubes
- Journal of Mathematical Imaging and Vision
- 2009

The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of… (More)

- Kim-Anh Lê Cao, Olivier Gonçalves, Philippe Besse, Sébastien Gadat
- Statistical applications in genetics and…
- 2007

We investigate an important issue of a meta-algorithm for selecting variables in the framework of microarray data. This wrapper method starts from any classification algorithm and weights each variable (i.e. gene) relative to its efficiency for classification. An optimization procedure is then inferred which exhibits important genes for the studied… (More)

- Kim-Anh Lê Cao, Agnès Bonnet, Sébastien Gadat
- Computational Statistics & Data Analysis
- 2009

Microarray technology allows for the monitoring of thousands of gene expressions in various biological conditions, but most of these genes are irrelevant for classifying these conditions. Feature selection is consequently needed to help reduce the dimension of the variable space. Starting from the application of the stochastic meta algorithm “Optimal… (More)

- Sébastien Gadat, Fabien Panloup
- 2012

In this paper, we are interested in a diffusion process based on a gradient descent. The process is non Markov and has a memory term which is built as a weighted average of the drift term all along the past of the trajectory. For this type of diffusion, we study the long time behaviour of the process in terms of the memory. We exhibit some conditions for… (More)

- Zilton Vasconcelos, Sabina Müller, +5 authors Loïc Dupré
- Cell reports
- 2015

The killing of antigen-bearing cells by clonal populations of cytotoxic T lymphocytes (CTLs) is thought to be a rapid phenomenon executed uniformly by individual CTLs. We combined bulk and single-CTL killing assays over a prolonged time period to provide the killing statistics of clonal human CTLs against an excess of target cells. Our data reveal… (More)

- Jérémie Bigot, Claire Christophe, Sébastien Gadat
- IEEE Transactions on Information Theory
- 2012

This paper considers the problem of estimating a mean pattern in the setting of Grenander's pattern theory. Shape variability in a dataset of curves or images is modeled by the random action of elements in a compact Lie group on an infinite dimensional space. In the case of observations contaminated by an additive Gaussian white noise, it is shown that… (More)

- Claire Christophe, Sabina Müller, +5 authors Salvatore Valitutti
- PloS one
- 2015

The dynamics of the interaction between Cytotoxic T Lymphocytes (CTL) and tumor cells has been addressed in depth, in particular using numerical simulations. However, stochastic mathematical models that take into account the competitive interaction between CTL and tumors undergoing immunoediting, a process of tumor cell escape from immunesurveillance, are… (More)

This paper considers the problem of adaptive estimation of a template in a randomly shifted curve model. Using the Fourier transform of the data, we show that this problem can be transformed into a linear inverse problem with a random operator. Our aim is to approach the estimator that has the smallest risk on the true template over a finite set of linear… (More)

- Sébastien Gadat, Fabien Panloup, Clément Pellegrini
- 2012

In this paper, we consider a class of diffusions based on a memory gradient descent, i.e. whose drift term is built as the average all along the past of the trajectory of the gradient of a coercive function U . Under some classical assumptions on U , this type of diffusion is ergodic and admits a unique invariant distribution. With the view to optimization… (More)