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no code implementations • ICML 2020 • Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts.

1 code implementation • NeurIPS 2021 • Vincent Mallet, Jean-Philippe Vert

As DNA sequencing technologies keep improving in scale and cost, there is a growing need to develop machine learning models to analyze DNA sequences, e. g., to decipher regulatory signals from DNA fragments bound by a particular protein of interest.

1 code implementation • NeurIPS 2021 • Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau

Building on the interpretation of a recurrent neural network (RNN) as a continuous-time neural differential equation, we show, under appropriate conditions, that the solution of a RNN can be viewed as a linear function of a specific feature set of the input sequence, known as the signature.

1 code implementation • NeurIPS 2021 • Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert

In this paper, we propose a unified, efficient and modular approach for implicit differentiation of optimization problems.

no code implementations • NeurIPS 2020 • Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).

1 code implementation • 16 Oct 2020 • Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert

Soft-DTW addresses these issues, but it is not a positive definite divergence: due to the bias introduced by entropic regularization, it can be negative and it is not minimized when the time series are equal.

1 code implementation • 10 Jun 2020 • Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert

Mixup is a data augmentation technique that creates new examples as convex combinationsof training points and labels.

no code implementations • 26 Apr 2020 • Marco Cuturi, Olivier Teboul, Quentin Berthet, Arnaud Doucet, Jean-Philippe Vert

Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting (tests can be mistaken) to decide adaptively (looking at past results) which groups to test next, with the goal to converge to a good detection, as quickly, and with as few tests as possible.

1 code implementation • 25 Feb 2020 • Imke Mayer, Julie Josse, Félix Raimundo, Jean-Philippe Vert

Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications.

no code implementations • 20 Feb 2020 • Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Machine learning pipelines often rely on optimization procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).

no code implementations • 8 Feb 2020 • Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts.

1 code implementation • NeurIPS 2019 • Marco Cuturi, Olivier Teboul, Jean-Philippe Vert

From this observation, we propose extended rank and sort operators by considering optimal transport (OT) problems (the natural relaxation for assignments) where the auxiliary measure can be any weighted measure supported on $m$ increasing values, where $m \ne n$.

no code implementations • 20 Oct 2019 • Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert

Clustering is a fundamental unsupervised learning approach.

1 code implementation • 21 Sep 2019 • Beyrem Khalfaoui, Joseph Boyd, Jean-Philippe Vert

Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability \emph{independently}.

no code implementations • 30 May 2019 • Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi, Lucas Beyer, Jean-Philippe Vert

We propose a learning algorithm capable of learning from label proportions instead of direct data labels.

no code implementations • 28 May 2019 • Marco Cuturi, Olivier Teboul, Jean-Philippe Vert

Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select closest neighbors and labels.

no code implementations • NeurIPS 2018 • Edouard Pauwels, Francis Bach, Jean-Philippe Vert

Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature.

2 code implementations • 26 Feb 2018 • 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 dropout events.

2 code implementations • ICML 2018 • Yunlong Jiao, Jean-Philippe Vert

We propose new positive definite kernels for permutations.

no code implementations • ICML 2018 • Marine Le Morvan, Jean-Philippe Vert

Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics.

no code implementations • 1 Jun 2017 • 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.

no code implementations • 24 Jun 2015 • Kévin Vervier, Pierre Mahé, Jean-Baptiste Veyrieras, Jean-Philippe Vert

Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems.

no code implementations • 26 May 2015 • Kévin Vervier, Pierre Mahé, Maud Tournoud, Jean-Baptiste Veyrieras, Jean-Philippe Vert

In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile.

no code implementations • NeurIPS 2014 • 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 known.

no code implementations • 12 May 2014 • Erwan Scornet, Gérard Biau, Jean-Philippe Vert

What has greatly contributed to the popularity of forests is the fact that they can be applied to a wide range of prediction problems and have few parameters to tune.

1 code implementation • 21 Jun 2011 • 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.

no code implementations • NeurIPS 2010 • Jean-Philippe Vert, Kevin Bleakley

We present a fast algorithm for the detection of multiple change-points when each is frequently shared by members of a set of co-occurring one-dimensional signals.

1 code implementation • 5 Oct 2010 • Fantine Mordelet, Jean-Philippe Vert

We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting.

no code implementations • NeurIPS 2009 • Marco Cuturi, Jean-Philippe Vert, Alexandre d'Aspremont

The candidate functionals are estimated in a subset of a reproducing kernel Hilbert space associated with the set where the process takes values.

no code implementations • NeurIPS 2008 • Laurent Jacob, Jean-Philippe Vert, Francis R. Bach

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 others.

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