11 citations · 29 across the 4 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
stat.AP2019
Explainable Clustering and Application to Wealth Management Compliance
Enguerrand Horel, Kay Giesecke, Victor Storchan +1
Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However,…
stat.ML2019★ 8 cited
Computationally Efficient Feature Significance and Importance for Machine Learning Models
Enguerrand Horel, Kay Giesecke
We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification,…
math.ST2019★ 9 cited
Significance Tests for Neural Networks
Enguerrand Horel, Kay Giesecke
We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based…