Search Results for author: Claire Boyer

Found 14 papers, 6 papers with code

Physics-informed machine learning as a kernel method

no code implementations12 Feb 2024 Nathan Doumèche, Francis Bach, Claire Boyer, Gérard Biau

In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency.

Physics-informed machine learning regression

An analysis of the noise schedule for score-based generative models

no code implementations7 Feb 2024 Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire

Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target. Recent literature has focused extensively on assessing the error between the target and estimated distributions, gauging the generative quality through the Kullback-Leibler (KL) divergence and Wasserstein distances.

Random features models: a way to study the success of naive imputation

no code implementations6 Feb 2024 Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data.


Sparse tree-based initialization for neural networks

no code implementations30 Sep 2022 Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet

Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data.


Minimax rate of consistency for linear models with missing values

no code implementations3 Feb 2022 Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys...).

Model-based Clustering with Missing Not At Random Data

1 code implementation20 Dec 2021 Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki

In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.

Clustering Imputation

Debiasing Averaged Stochastic Gradient Descent to handle missing values

no code implementations NeurIPS 2020 Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josses

Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.

Analyzing the tree-layer structure of Deep Forests

no code implementations29 Oct 2020 Ludovic Arnould, Claire Boyer, Erwan Scornet, Sorbonne Lpsm

Random forests on the one hand, and neural networks on the other hand, have met great success in the machine learning community for their predictive performance.

Missing Data Imputation using Optimal Transport

1 code implementation ICML 2020 Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi

Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.


Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data

1 code implementation NeurIPS 2020 Aude Sportisse, Claire Boyer, Julie Josse

Considering a data matrix generated from a probabilistic principal component analysis (PPCA) model containing several MNAR variables, not necessarily under the same self-masked missing mechanism, we propose estimators for the means, variances and covariances of the variables and study their consistency.

Statistics Theory Statistics Theory

Imputation and low-rank estimation with Missing Not At Random data

1 code implementation29 Dec 2018 Aude Sportisse, Claire Boyer, Julie Josse

Our second contribution is to suggest a computationally efficient surrogate estimation by implicitly taking into account the joint distribution of the data and the missing mechanism: the data matrix is concatenated with the mask coding for the missing values; a low-rank structure for exponential family is assumed on this new matrix, in order to encode links between variables and missing mechanisms.

Imputation Matrix Completion

Proximal boosting: aggregating weak learners to minimize non-differentiable losses

no code implementations29 Aug 2018 Erwan Fouillen, Claire Boyer, Maxime Sangnier

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model.

On oracle-type local recovery guarantees in compressed sensing

1 code implementation11 Jun 2018 Ben Adcock, Claire Boyer, Simone Brugiapaglia

We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing.

Information Theory Information Theory

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