104 citations · 200 across the 6 of their papers we have counts for
6 papers
AMLB: an AutoML Benchmark
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors +5
Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids co…
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…
Automatic Componentwise Boosting: An Interpretable AutoML System
Stefan Coors, Daniel Schalk, Bernd Bischl +1
In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model e…
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Bernd Bischl, Martin Binder, Michel Lang +9
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming…
Multi-Objective Automatic Machine Learning with AutoxgboostMC
Florian Pfisterer, Stefan Coors, Janek Thomas +1
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machin…
Automatic Gradient Boosting
Janek Thomas, Stefan Coors, Bernd Bischl
Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference. This is achieved by making machine learning applicat…