1.1k citations · 1.1k across the 5 of their papers we have counts for
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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…
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager +3
Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been p…
Tackling Neural Architecture Search With Quality Diversity Optimization
Lennart Schneider, Florian Pfisterer, Paul Kent +3
Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for th…
mlr Tutorial
Julia Schiffner, Bernd Bischl, Michel Lang +9
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.