J
Joaquin Vanschoren
Researcher at Eindhoven University of Technology
Publications - 154
Citations - 5001
Joaquin Vanschoren is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Computer science & Meta learning (computer science). The author has an hindex of 28, co-authored 119 publications receiving 3303 citations. Previous affiliations of Joaquin Vanschoren include Leiden University & Katholieke Universiteit Leuven.
Papers
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Journal ArticleDOI
OpenML: networked science in machine learning
TL;DR: OpenML as discussed by the authors is a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems.
Book
Automated Machine Learning
TL;DR: This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems.
Posted Content
Meta-Learning: A Survey
TL;DR: This chapter provides an overview of the state of the art in meta-learning, the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible.
Journal ArticleDOI
OpenML: networked science in machine learning
TL;DR: This paper introduces OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems.
Journal ArticleDOI
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl,Pascal Kerschke,Lars Kotthoff,Marius Lindauer,Yuri Malitsky,Alexandre Fréchette,Holger H. Hoos,Frank Hutter,Kevin Leyton-Brown,Kevin Tierney,Joaquin Vanschoren +10 more
TL;DR: In this article, the authors introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature, and demonstrate the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.