output
20082025
most citedClustered Multi-Task Learning: A Convex Formulation

285 citations

17 papers

stat.AP2025

A note on a resampling procedure for estimating the density at a given quantile

Beatriz Farah, Aurélien Latouche, Olivier Bouaziz

In this paper we refine the procedure proposed by Lin et al. (2015) to estimate the density at a given quantile based on a resampling method. The approach consists on generating mu…

cs.CV2025★ 2 cited

MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models

Guillaume Balezo, Roger Trullo, Albert Pla Planas +2

Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tis…

eess.IV2024★ 2 cited

Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging

Simon Gutwein, Daria Lazic, Thomas Walter +2

Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights int…

cs.LG2020★ 8 cited

Local intrinsic dimensionality estimators based on concentration of measure

Jonathan Bac, Andrei Zinovyev

Intrinsic dimensionality (ID) is one of the most fundamental characteristics of multi-dimensional data point clouds. Knowing ID is crucial to choose the appropriate machine learnin…

math.OC2018★ 2 cited

Robust Seriation and Applications to Cancer Genomics

Antoine Recanati, Nicolas Servant, Jean-Philippe Vert +1

The seriation problem seeks to reorder a set of elements given pairwise similarity information, so that elements with higher similarity are closer in the resulting sequence. When a…

q-bio.QM2018★ 3 cited

DropLasso: A robust variant of Lasso for single cell RNA-seq data

Beyrem Khalfaoui, Jean-Philippe Vert

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many…

q-bio.QM2018★ 9 cited

WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models

Marine Le Morvan, Jean-Philippe Vert

Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics. l1-regularised linear models are popular to estimate sparse model…

stat.ML2018★ 2 cited

The Weighted Kendall and High-order Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert

We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of differ…

stat.ML2017

Supervised Quantile Normalisation

Marine Le Morvan, Jean-Philippe Vert

Quantile normalisation is a popular normalisation method for data subject to unwanted variations such as images, speech, or genomic data. It applies a monotonic transformation to t…

stat.ML2016★ 5 cited

Network-Guided Biomarker Discovery

Chloé-Agathe Azencott

Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnost…

q-bio.GN2016★ 8 cited

Dynamic read mapping and online consensus calling for better variant detection

Karel Břinda, Valentina Boeva, Gregory Kucherov

Variant detection from high-throughput sequencing data is an essential step in identification of alleles involved in complex diseases and cancer. To deal with these massive data, e…

stat.ML2014★ 23 cited

Tight convex relaxations for sparse matrix factorization

Emile Richard, Guillaume Obozinski, Jean-Philippe Vert

Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and…

q-bio.QM2011★ 136 cited

The group fused Lasso for multiple change-point detection

Kevin Bleakley, Jean-Philippe Vert

We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the or…

stat.ML2010★ 9 cited

Increasing stability and interpretability of gene expression signatures

Anne-Claire Haury, Laurent Jacob, Jean-Philippe Vert

Motivation : Molecular signatures for diagnosis or prognosis estimated from large-scale gene expression data often lack robustness and stability, rendering their biological interpr…

q-bio.QM2009★ 3 cited

Joint segmentation of many aCGH profiles using fast group LARS

Kevin Bleakley, Jean-Philippe Vert

Array-Based Comparative Genomic Hybridization (aCGH) is a method used to search for genomic regions with copy numbers variations. For a given aCGH profile, one challenge is to accu…

cs.LG2008★ 285 cited

Clustered Multi-Task Learning: A Convex Formulation

Laurent Jacob, Francis Bach, Jean-Philippe Vert

In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the…

cs.LG2008★ 51 cited

The optimal assignment kernel is not positive definite

Jean-Philippe Vert

We prove that the optimal assignment kernel, proposed recently as an attempt to embed labeled graphs and more generally tuples of basic data to a Hilbert space, is in fact not alwa…