45 citations · 121 across the 21 of their papers we have counts for
7 papers · 1 filter
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…
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…
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,…
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…
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…