Machine learning has achieved considerable successes in recent years, but these successes crucially rely on human machine learning experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. As the complexity of these tasks is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
AutoML aims to automate many different stages of the machine learning process such as:
We especially encourage demos of working AutoML systems; demo proposals are submitted through an accompanying paper. We also encourage the participants of the AutoML challenge (http://automl.chalearn.org/) to submit a paper.
The best 2-3 papers will be invited for oral plenary presentation. All other accepted papers will be presented as posters and short poster spotlight presentations. We plan to invite the authors of high-quality submissions to submit extended versions of their work for another round of reviews and publication in the post-workshop proceedings. For submission details please see the submission page.
For authors who don't need the acceptance notice early, we have a 2nd "late breaking paper" submission cycle. The only difference between this and the first submission is in the dates of submission and acceptance notification. Note that rejects from the first deadline will not be eligible for resubmission.