6 citations · 8 across the 6 of their papers we have counts for
6 papers
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou +3
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks' parameter space. Our work shows that successful SBI is pos…
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio +3
We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explai…
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models
Daniel Schalk, Bernd Bischl, David Rügamer
Various privacy-preserving frameworks that respect the individual's privacy in the analysis of data have been developed in recent years. However, available model classes such as si…
Developing Open Source Educational Resources for Machine Learning and Data Science
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio +4
Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machi…
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
Martin Binder, Julia Moosbauer, Janek Thomas +1
Both feature selection and hyperparameter tuning are key tasks in machine learning. Hyperparameter tuning is often useful to increase model performance, while feature selection is…