20 citations · 22 across the 6 of their papers we have counts for
6 papers
Empirically Calibrated Conditional Independence Tests
Milleno Pan, Antoine de Mathelin, Wesley Tansey
Conditional independence tests (CIT) are widely used for causal discovery and feature selection. Even with false discovery rate (FDR) control procedures, they often fail to provide…
Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin, Christopher Tosh, Wesley Tansey
Personalizing combination therapies in oncology requires navigating an immense space of possible drug and dose combinations, a task that remains largely infeasible through exhausti…
Fast and Accurate Importance Weighting for Correcting Sample Bias
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot +1
Bias in datasets can be very detrimental for appropriate statistical estimation. In response to this problem, importance weighting methods have been developed to match any biased d…
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt +1
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This…
ADAPT : Awesome Domain Adaptation Python Toolbox
Antoine de Mathelin, Mounir Atiq, Guillaume Richard +5
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is de…
Adversarial Weighting for Domain Adaptation in Regression
Antoine de Mathelin, Guillaume Richard, Francois Deheeger +2
We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed…