
Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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Differentiable Divergences Between Time Series
Computing the discrepancy between time series of variable sizes is notor...
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On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
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Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
When test resources are scarce, a viable alternative to test for the pre...
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MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from obs...
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Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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Supervised Quantile Normalization for Lowrank Matrix Approximation
Low rank matrix factorization is a fundamental building block in machine...
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Differentiable Deep Clustering with Cluster Size Constraints
Clustering is a fundamental unsupervised learning approach. Many cluster...
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ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks
Dropout is a regularisation technique in neural network training where u...
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Deep multiclass learning from label proportions
We propose a learning algorithm capable of learning from label proportio...
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Differentiable Sorting using Optimal Transport:The Sinkhorn CDF and Quantile Operator
Sorting an array is a fundamental routine in machine learning, one that ...
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Relating Leverage Scores and Density using Regularized Christoffel Functions
Statistical leverage scores emerged as a fundamental tool for matrix ske...
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DropLasso: A robust variant of Lasso for single cell RNAseq data
Singlecell RNA sequencing (scRNAseq) is a fast growing approach to mea...
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The Weighted Kendall and Highorder Kernels for Permutations
We propose new positive definite kernels for permutations. First we intr...
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WHInter: A Working set algorithm for Highdimensional sparse second order Interaction models
Learning sparse linear models with twoway interactions is desirable in ...
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Supervised Quantile Normalisation
Quantile normalisation is a popular normalisation method for data subjec...
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Benchmark of structured machine learning methods for microbial identification from massspectrometry data
Microbial identification is a central issue in microbiology, in particul...
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Largescale Machine Learning for Metagenomics Sequence Classification
Metagenomics characterizes the taxonomic diversity of microbial communit...
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Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for spar...
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Consistency of random forests
Random forests are a learning algorithm proposed by Breiman [Mach. Learn...
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TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
Inferring the structure of gene regulatory networks (GRN) from gene expr...
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Group Lasso with Overlaps: the Latent Group Lasso approach
We study a norm for structured sparsity which leads to sparse linear pre...
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The group fused Lasso for multiple changepoint detection
We present the group fused Lasso for detection of multiple changepoints...
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ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
Elucidating the genetic basis of human diseases is a central goal of gen...
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The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
Motivation: Biomarker discovery from highdimensional data is a crucial ...
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A bagging SVM to learn from positive and unlabeled examples
We consider the problem of learning a binary classifier from a training ...
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ManytoMany Graph Matching: a Continuous Relaxation Approach
Graphs provide an efficient tool for object representation in various co...
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Increasing stability and interpretability of gene expression signatures
Motivation : Molecular signatures for diagnosis or prognosis estimated f...
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A path following algorithm for the graph matching problem
We propose a convexconcave programming approach for the labeled weighte...
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A kernel for time series based on global alignments
We propose in this paper a new family of kernels to handle times series,...
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JeanPhilippe Vert
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Professor at École normale supérieure, Professor at Higher Normal School since 2016, Deputy director U900 at Institut Curie since 2008, Director at Center for Computational Biology, Ecole des Mines de Paris since 2006, Visiting Scholar at UC Berkeley from 20152016, Visiting Scientist at Genentech 2016, Bioinformatics Group leader at Ecole des Mines de Paris from 20022005, Associate researcher at Kyoto University, Bioinformatics Center from 20012002, postdoc at Kyoto University from 20012002, Research scientist at Atofina from 19961997