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AutoML: taking the human expert out of the loop
AutoML.org is no longer updated. We moved all information over to ml4aad.org/automl.
We're organizing an AutoML workshop at ICML 2016.
We're organizing an AutoML workshop at ICML 2015.
We organized an AutoML workshop at ICML 2014.
Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:
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.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
Following the paradigm of Programming by Optimization, AutoML advocates the development of flexible software packages that can be instantiated automatically in a data-driven way.
AutoML aims to create software that can be used out-of-the-box by ML novices. Some recent examples showcase what is possible:
AutoML draws on many disciplines of machine learning, prominently including
Several recent systems for the Bayesian optimization of machine learning hyperparameters facilitate AutoML. These include:
We offer a common interface to these in the hyperparameter optimization library, HPOlib.