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. 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.

We invite submissions on the topics of:

* Model selection, hyper-parameter optimization, and model search
* Neural architecture search
* Meta-learning and transfer learning
* Learning to learn new algorithms and strategies
* Automation of any element of the ML pipeline, including: feature extraction / construction, data cleaning, generation of workflows / workflow reuse, problem "ingestion" (from raw data and miscellaneous formats), acquisition of new data (active learning, experimental design), report generation (providing insight on automated data analysis), selection of evaluation metrics / validation procedures, selection of algorithms under time/space/power constraints, construction of fair and unbiased machine learning models, semi-supervised and unsupervised machine learning
* Extending the scope of AutoML towards automated data science
* Human-in-the-loop approaches for AutoML
* Demos of existing AutoML systems
* Robustness of AutoML systems (w.r.t. randomized algorithms, data, hardware etc.)
* Hyperparameter agnostic algorithms

We welcome submissions up to 6 pages in JMLR format (+ references). We strongly encourage attachments of code to foster reproducibility; reproducibility of results and easy availability of code will be taken into account in the decision making process. All accepted papers will be presented as posters. We may invite the best 2-3 papers for an oral plenary presentation. Unless indicated by the authors, we will provide PDFs of all accepted papers on http://icml2019.automl.org/. There will be no archival proceedings. For submission details please see the submission page.
Confirmed Speakers

* Rachel Thomas
* Raquel Urtasun
* Charles Sutton

Location

The 6th ICML AutoML workshop will be co-located with the 36th International Conference on Machine Learning (ICML 2019) in Long Beach, CA, USA and will take place on June 14 or June 15. Please check the practical information page for further information.
Tentative Dates

* April 1st: submission system opens
* May 2nd: submission deadline
* May 17th: notification
* June 14th or 15th: workshop day