5 papers
Unbiased Simulation Estimators for Multivariate Jump-Diffusions
Guanting Chen, Alex Shkolnik, Kay Giesecke
We develop and analyze a class of unbiased Monte Carlo estimators for multivariate jump-diffusion processes with state-dependent drift, volatility, jump intensity and jump size. A…
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…