1 code implementation • 2 Feb 2024 • Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman

Spherical harmonics are polynomials on the sphere that form an orthonormal basis of the set of square-integrable functions on the sphere.

no code implementations • 15 Dec 2023 • François Portier, Lionel Truquet, Ikko Yamane

Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution.

no code implementations • 23 Oct 2023 • Anass Aghbalou, François Portier, Anne Sabourin

When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure.

no code implementations • 24 May 2022 • Rémi Leluc, François Portier, Johan Segers, Aigerim Zhuman

Within the standard adaptive importance sampling framework, a simple weighted least squares approach is proposed to improve the procedure with control variates.

no code implementations • 29 Oct 2021 • Anna Korba, François Portier

Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate the so-called target distribution.

no code implementations • 27 Oct 2021 • François Portier

First, the associated empirical process is shown to satisfy a uniform central limit theorem under a local bracketing entropy condition on the underlying class of functions reflecting the localizing nature of the nearest neighbor algorithm.

1 code implementation • 25 May 2021 • Rémi Leluc, François Portier

While classical forms of stochastic gradient descent algorithm treat the different coordinates in the same way, a framework allowing for adaptive (non uniform) coordinate sampling is developed to leverage structure in data.

no code implementations • 26 Jun 2020 • Guillaume Ausset, Stephan Clémençon, François Portier

Motivated by a wide variety of applications, ranging from stochastic optimization to dimension reduction through variable selection, the problem of estimating gradients accurately is of crucial importance in statistics and learning theory.

no code implementations • 23 Jun 2020 • Pascal Bianchi, Kevin Elgui, François Portier

This paper introduces the \textit{weighted partial copula} function for testing conditional independence.

no code implementations • 16 Jun 2020 • Vincent Plassier, François Portier, Johan Segers

Consider the problem of learning a large number of response functions simultaneously based on the same input variables.

1 code implementation • 4 Jun 2020 • Rémi Leluc, François Portier

In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction.

1 code implementation • 24 Oct 2019 • Mohammed Bouchouia, François Portier

A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic.

no code implementations • 26 Jun 2019 • Rémi Leluc, François Portier, Johan Segers

Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regression model with the integrand as response and the control variates as covariates.

Statistics Theory Statistics Theory

no code implementations • 5 Jun 2019 • Guillaume Ausset, Stéphan Clémençon, François Portier

As ignoring censorship in the risk computation may clearly lead to a severe underestimation of the target duration and jeopardize prediction, we propose to consider a plug-in estimate of the true risk based on a Kaplan-Meier estimator of the conditional survival function of the censorship $C$ given $X$, referred to as Kaplan-Meier risk, in order to perform empirical risk minimization.

1 code implementation • NeurIPS 2018 • Bernard Delyon, François Portier

Each stage $t$ is formed with two steps : (i) to explore the space with $n_t$ points according to $q_t$ and (ii) to exploit the current amount of information to update the sampling policy.

1 code implementation • 4 Jun 2018 • François Portier, Ingrid Van Keilegom, Anouar El Ghouch

We consider the problem of estimating the distribution of time-to-event data that are subject to censoring and for which the event of interest might never occur, i. e., some subjects are cured.

Statistics Theory Methodology Statistics Theory

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