11 citations · 29 across the 4 of their papers we have counts for
5 papers
AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
Machine learning is central to modern science, industry, and policy, yet its predictive power often comes at the cost of transparency: we rarely know which input features truly dri…
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,…
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,…
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…
Sensitivity based Neural Networks Explanations
Enguerrand Horel, Virgile Mison, Tao Xiong +2
Although neural networks can achieve very high predictive performance on various different tasks such as image recognition or natural language processing, they are often considered…