3 papers
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
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,…
stat.ML2018
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…