State-of-the-art machine learning/AI systems consist of complex pipelines with choices of hyperparameters, models and configuration details that need to be tuned for optimal performance. The resulting optimization space can be too complex and high-dimensional for researchers and engineers to explore manually. When automated systems are used, the high costs of running a single experiment (e.g. training a deep neural network) and the high sample complexity (i.e. large number of experiments required) together make naïve approaches impractical.

Many of the problems we are interested in can be cast as high-dimensional combinatorial optimization tasks.  Broadly speaking, we tackle these problems by designing probabilistic machine learning models to guide (automated) experimental decisions and meta-learning to reduce the sample complexity and transfer knowledge across related datasets or problems.

Specific problems that the Microsoft Research AutoML team focuses on include:

Neural architecture search
Model selection
Feature engineering
Hyperparameter tuning
Model compression


Core AutoML technology is already live in Azure Machine Learning, Power BI and other Microsoft products.




Current interns

In the news

Blogs & podcasts

All about automated machine learning with Dr. Nicolo Fusi

You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters…

Microsoft Research Podcast | Episode 43 | Septemer 26, 2018

Announcing automated ML capability in Azure Machine Learning

Intelligent experiences powered by machine learning can seem like magic to users. Developing them, however, can be anything but. Consider this “simple” tutorial chart from the scikit-learn machine learning library…

Microsoft Azure Blog | Septemer 26, 2018