Practical and sample efficient zero-shot HPO

no code implementations • 27 Jul 2020 • Fela Winkelmolen, Nikita Ivkin, H. Furkan Bozkurt, Zohar Karnin

Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement each other.

Hyperparameter Optimization Transfer Learning

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization

no code implementations • 15 Dec 2020 • Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias Seeger, Cédric Archambeau

To democratize access to machine learning systems, it is essential to automate the tuning.

Bayesian Optimization BIG-bench Machine Learning

Amazon SageMaker Autopilot: a white box AutoML solution at scale

no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel

AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.

AutoML Meta-Learning