8th ICML Workshop on Automated Machine Learning (AutoML)

Machine learning has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for off-the-shelf machine learning methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. Hence, the workshop targets a broad audience ranging from core machine learning researchers in different fields of ML connected to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and learning to learn, to domain experts aiming to apply machine learning to new types of problems.

Keynote Speakers

Main Developer of auto-sklearn: Winner of the 1st and 2nd AutoML competition and the warmstarting-friendly BBO challenge@NeurIPS'20

Title: Towards hands-free AutoML

Data Scientist, H2O.ai, Applied Mathematician, and Kaggle Grandmaster

Title: Bias, Controlling Bias, and AutoML

Miller Fellow at UC Berkeley and soon-to-be Assistant Professor at Stanford, joint between the Management Sciences and Engineering department and Computer Science department (starting September 2022).

Title: Automated Parameter Optimization for Integer Programming

Associate Professor, Director of Machine Learning Systems (MLSys) Group at Michigan State University

Title: Encoding is an Important Design Decision in Neural Architecture Search

Call for Papers and Important Dates

Please see the Call for papers and Submission instructions.

Workshop Format

Due to COVID-19 the workshop will be completely virtual. We will update details of the format shortly.


Katharina Eggensperger, Frank Hutter, Erin LeDell, Marius Lindauer, Gresa Shala, Joaquin Vanschoren and Colin White