25 citations · 95 across the 17 of their papers we have counts for
19 papers
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Alexander G. Reisach, Myriam Tami, Christof Seiler +2
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking.…
Inference in Marginal Structural Models by Automatic Targeted Bayesian and Minimum Loss-Based Estimation
Herbert Susmann, Antoine Chambaz
Two of the principle tasks of causal inference are to define and estimate the effect of a treatment on an outcome of interest. Formally, such treatment effects are defined as a pos…
Forecasting the cost of drought events in France by Super Learning
Geoffrey Ecoto, Antoine Chambaz
Drought events are the second most expensive type of natural disaster within the French legal framework known as the natural disasters compensation scheme. In recent years, drought…
Personalized Online Machine Learning
Ivana Malenica, Rachael V. Phillips, Romain Pirracchio +3
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degre…
One-step ahead sequential Super Learning from short times series of many slightly dependent data, and anticipating the cost of natural disasters
Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz
Suppose that we observe a short time series where each time-t-specific data-structure consists of many slightly dependent data indexed by a and that we want to estimate a feature o…
Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data
Thi Thanh Yen Nguyen, Warith Harchaoui, Lucile Mégret +4
We present several algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit a relationship…