629 citations · 920 across the 6 of their papers we have counts for
6 papers
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…
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…
Improving Adaptive Seamless Designs through Bayesian optimization
Jakob Richter, Tim Friede, Jörg Rahnenführer
We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions,…
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa +2
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification pr…
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Bernd Bischl, Jakob Richter, Jakob Bossek +3
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-bo…
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.