115 citations · 317 across the 17 of their papers we have counts for
22 papers
What Can AutoML Do For Continual Learning?
Mert Kilickaya, Joaquin Vanschoren
This position paper outlines the potential of AutoML for incremental (continual) learning to encourage more research in this direction. Incremental learning involves incorporating…
Automated Imbalanced Learning
Prabhant Singh, Joaquin Vanschoren
Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when…
Warm-starting DARTS using meta-learning
Matej Grobelnik, Joaquin Vanschoren
Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a significant ste…
Advances in MetaDL: AAAI 2021 challenge and workshop
Adrian El Baz, Isabelle Guyon, Zhengying Liu +3
To stimulate advances in metalearning using deep learning techniques (MetaDL), we organized in 2021 a challenge and an associated workshop. This paper presents the design of the ch…
Frugal Machine Learning
Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos +2
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet…
From Strings to Data Science: a Practical Framework for Automated String Handling
John W. van Lith, Joaquin Vanschoren
Many machine learning libraries require that string features be converted to a numerical representation for the models to work as intended. Categorical string features can represen…