45 citations · 121 across the 21 of their papers we have counts for
22 papers
A primer on linear classification with missing data
Angel D Reyero Lobo, Alexis Ayme, Claire Boyer +1
Supervised learning with missing data aims at building the best prediction of a target output based on partially-observed inputs. Major approaches to address this problem can be de…
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce…
Naive imputation implicitly regularizes high-dimensional linear models
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Two different approaches exist to handle missing values for prediction: either imputation, prior to fitting any predictive algorithms, or dedicated methods able to natively incorpo…
Sparse tree-based initialization for neural networks
Patrick Lutz, Ludovic Arnould, Claire Boyer +1
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 metho…
Minimax rate of consistency for linear models with missing values
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
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.…
Is interpolation benign for random forest regression?
Ludovic Arnould, Claire Boyer, Erwan Scornet
Statistical wisdom suggests that very complex models, interpolating training data, will be poor at predicting unseen examples.Yet, this aphorism has been recently challenged by the…
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions
Claire Boyer, Antoine Godichon-Baggioni
The majority of machine learning methods can be regarded as the minimization of an unavailable risk function. To optimize the latter, given samples provided in a streaming fashion,…
Analyzing the tree-layer structure of Deep Forests
Ludovic Arnould, Claire Boyer, Erwan Scornet +1
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. Combinations of bo…
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
Pascaline Descloux, Claire Boyer, Julie Josse +2
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology, initially introduced for sparse linear models, to the sparse corruptions problem. We give theoretical guar…
Sampling Rates for -Synthesis
Maximilian März, Claire Boyer, Jonas Kahn +1
This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian measurements under the assumption of a synthesis-based sparsity model. Solving the $\ell^…
Debiasing Stochastic Gradient Descent to handle missing values
Julie Josse, Aude Sportisse, Claire Boyer +1
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.However, a major caveat of large data is their…
Missing Data Imputation using Optimal Transport
Boris Muzellec, Julie Josse, Claire Boyer +1
Missing data is a crucial issue when applying machine learning algorithms to real-world datasets. Starting from the simple assumption that two batches extracted randomly from the s…
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing Not At Random (MNAR) values lead to significant biases in the data, since the probability of missingness depends on the unobserved values.They are ''not ignorable'' in the…
Convex Regularization and Representer Theorems
Claire Boyer, Antonin Chambolle, Yohann de Castro +3
We establish a result which states that regularizing an inverse problem with the gauge of a convex set yields solutions which are linear combinations of a few extreme points or…
Imputation and low-rank estimation with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank…
On Representer Theorems and Convex Regularization
Claire Boyer, Antonin Chambolle, Yohann De Castro +3
We establish a general principle which states that regularizing an inverse problem with a convex function yields solutions which are convex combinations of a small number of atoms.…
On oracle-type local recovery guarantees in compressed sensing
Ben Adcock, Claire Boyer, Simone Brugiapaglia
We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on l1 minimization encompasses the case where…
Adapting to unknown noise level in sparse deconvolution
Claire Boyer, Yohann De Castro, Joseph Salmon
In this paper, we study sparse spike deconvolution over the space of complex-valued measures when the input measure is a finite sum of Dirac masses. We introduce a modified version…
Compressed sensing with structured sparsity and structured acquisition
Claire Boyer, Jérémie Bigot, Pierre Weiss
Compressed Sensing (CS) is an appealing framework for applications such as Magnetic Resonance Imaging (MRI). However, up-to-date, the sensing schemes suggested by CS theories are m…
An algorithm for variable density sampling with block-constrained acquisition
Claire Boyer, Pierre Weiss, Jérémie Bigot
Reducing acquisition time is of fundamental importance in various imaging modalities. The concept of variable density sampling provides a nice framework to achieve this. It was jus…
An analysis of block sampling strategies in compressed sensing
Jérémie Bigot, Claire Boyer, Pierre Weiss
Compressed sensing is a theory which guarantees the exact recovery of sparse signals from a small number of linear projections. The sampling schemes suggested by current compressed…