168 citations · 380 across the 21 of their papers we have counts for
7 papers · 1 filter
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Matthias Feurer, Katharina Eggensperger, Edward Bergman +3
Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperp…
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
David Rügamer, Florian Pfisterer, Bernd Bischl +1
In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimi…
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…
A geometric framework for outlier detection in high-dimensional data
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Florian Karl, Tobias Pielok, Julia Moosbauer +10
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preproc…