2 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Guillaume Martinet, Alexander Strzalkowski, Barbara E. Engelhardt
Selecting powerful predictors for an outcome is a cornerstone task for machine learning. However, some types of questions can only be answered by identifying the predictors that ca…
math.ST2020★ 2 cited
A Balancing Weight Framework for Estimating the Causal Effect of General Treatments
Guillaume Martinet
In observational studies, weighting methods that directly optimize the balance between treatment and covariates have received much attention lately; however these have mainly focus…
stat.ML2018
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
Samory Kpotufe, Guillaume Martinet
We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target la…