1 citations · 1 across the 6 of their papers we have counts for
6 papers
Handling Covariate Mismatch in Collaborative Linear Prediction
Alexis Ayme, Rémi Khellaf
Training predictive models across multiple centers typically assumes that all centers collect the same set of covariates. In practice, however, they may record different features o…
Breaking the curse of dimensionality for linear rules: optimal predictors over the ellipsoid
Alexis Ayme, Bruno Loureiro
In this work, we address the following question: What minimal structural assumptions are needed to prevent the degradation of statistical learning bounds with increasing dimensiona…
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…
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.…