25 citations · 44 across the 7 of their papers we have counts for
7 papers · 1 filter
Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
Xin Du, Sikun Yang, Wouter Duivesteijn +1
Understanding the nuanced performance of machine learning models is essential for responsible deployment, especially in high-stakes domains like healthcare and finance. This paper…
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn +2
Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting…
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring
Sibylle Hess, Wouter Duivesteijn, Decebal Mocanu
We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been…
k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
Sibylle Hess, Wouter Duivesteijn
Most convex and nonconvex clustering algorithms come with one crucial parameter: the in -means. To this day, there is not one generally accepted way to accurately determine…
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
Sibylle Hess, Wouter Duivesteijn, Philipp Honysz +1
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporatin…
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
Xin Du, Lei Sun, Wouter Duivesteijn +2
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a…
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally
Oren Zeev-Ben-Mordehai, Wouter Duivesteijn, Mykola Pechenizkiy
Finding regions for which there is higher controversy among different classifiers is insightful with regards to the domain and our models. Such evaluation can falsify assumptions,…