Search Results - "Celisse, Alain"

MLGL : An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso by Grimonprez, Quentin, Blanck, Samuel, Celisse, Alain, Marot, Guillemette

“…The MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory…”
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Consistency of maximum-likelihood and variational estimators in the stochastic block model by Celisse, Alain, Daudin, Jean-Jacques, Pierre, Laurent

“…The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the…”
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Segmentation of the mean of heteroscedastic data via cross-validation by Arlot, Sylvain, Celisse, Alain

“…This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of…”
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Nonparametric density estimation by exact leave- p -out cross-validation by Celisse, Alain, Robin, Stéphane

“…The problem of density estimation is addressed by minimization of the L 2 - risk for both histogram and kernel estimators. This quadratic risk is estimated by…”
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A cross-validation based estimation of the proportion of true null hypotheses by Celisse, Alain, Robin, Stéphane

“…In the multiple testing context, a challenging problem is the estimation of the proportion π 0 of true null hypotheses. A large number of estimators of this…”
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MPAgenomics: an R package for multi-patient analysis of genomic markers by Grimonprez, Quentin, Celisse, Alain, Blanck, Samuel, Cheok, Meyling, Figeac, Martin, Marot, Guillemette

“…Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures. MPAgenomics, standing…”
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Optimal cross-validation in density estimation with the $L^{2}$-loss by Celisse, Alain

“…We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The…”
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Analyzing the discrepancy principle for kernelized spectral filter learning algorithms by Celisse, Alain, Wahl, Martin

“…We investigate the construction of early stopping rules in the nonparametric regression problem where iterative learning algorithms are used and the optimal…”
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Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation by Guedj, Mickael, Robin, Stephane, Celisse, Alain, Nuel, Gregory

“…The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and,…”
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OPTIMAL CROSS-VALIDATION IN DENSITY ESTIMATION WITH THE L²-LOSS by Celisse, Alain

“…We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The…”
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Optimal cross-validation in density estimation with the L^sup 2^-loss by Celisse, Alain

“…We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The…”
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New efficient algorithms for multiple change-point detection with reproducing kernels by Celisse, A., Marot, G., Pierre-Jean, M., Rigaill, G.J.

“…Several statistical approaches based on reproducing kernels have been proposed to detect abrupt changes arising in the full distribution of the observations…”
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MPAgenomics : An R package for multi-patients analysis of genomic markers by Grimonprez, Quentin, Celisse, Alain, Blanck, Samuel, Cheok, Meyling, Figeac, Martin, Marot, Guillemette

“…MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker…”
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Nonparametric density estimation by exact leave--out cross-validation by Celisse, Alain, Robin, Stéphane

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Breakpoint based online anomaly detection by Krönert, Etienne, Hattab, Dalila, Celisse, Alain

“…The goal of anomaly detection is to identify observations that are generated by a distribution that differs from the reference distribution that qualifies…”
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Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression by Averyanov, Yaroslav, Celisse, Alain

“…We present a novel data-driven strategy to choose the hyperparameter $k$ in the $k$-NN regression estimator without using any hold-out data. We treat the…”
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Early stopping and polynomial smoothing in regression with reproducing kernels by Averyanov, Yaroslav, Celisse, Alain

“…In this paper, we study the problem of early stopping for iterative learning algorithms in a reproducing kernel Hilbert space (RKHS) in the nonparametric…”
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A semi-parametric kernel-based approach to local False Discovery Rate estimations by Nuel, Gregory, Celisse, Alain, Guedj, Mickaël, Robin, Stéphane

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Stability revisited: new generalisation bounds for the Leave-one-Out by Celisse, Alain, Guedj, Benjamin

“…The present paper provides a new generic strategy leading to non-asymptotic theoretical guarantees on the Leave-one-Out procedure applied to a broad class of…”
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A One-Sample Test for Normality with Kernel Methods by Kellner, Jérémie, Celisse, Alain

“…We propose a new one-sample test for normality in a Reproducing Kernel Hilbert Space (RKHS). Namely, we test the null-hypothesis of belonging to a given family…”
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