7 citations · 8 across the 6 of their papers we have counts for
7 papers
A Bregman Perspective on Classification and Regression Trees
Mathias Bourel
Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of impurity measures have been proposed for…
Geometric Domain Adaptation via Optimal Transport for Linear Regression in R^2
Brian Britos, Mathias Bourel
Optimal Transport has become recently a powerful method for domain adaptation by aligning source and target distributions. We study a supervised domain adaptation problem where sou…
Optimal Transport-Based Domain Adaptation for Rotated Linear Regression
Brian Britos, Mathias Bourel
Optimal Transport (OT) has proven effective for domain adaptation (DA) by aligning distributions across domains with differing statistical properties. Building on the approach of C…
Comparing partitions through the Matching Error
Mathias Bourel, Badih Ghattas, Meliza González
With the aim to propose a non parametric hypothesis test, this paper carries out a study on the Matching Error (ME), a comparison index of two partitions obtained from the same dat…
Bagging of Density Estimators
Mathias Bourel, Jairo Cugliari
In this work we give new density estimators by averaging classical density estimators such as the histogram, the frequency polygon and the kernel density estimators obtained over d…
Aggregating density estimators: an empirical study
Mathias Bourel, Badih Ghattas
We present some new density estimation algorithms obtained by bootstrap aggregation like Bagging. Our algorithms are analyzed and empirically compared to other methods found in the…