25 citations · 44 across the 7 of their papers we have counts for
8 papers
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,…
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn +3
Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combin…