Daniel Golovin
Daniel Golovin currently leads the Google DeepMind group located in Pittsburgh. He works broadly on machine learning driven optimization and automated experimental design -- looking at how we can leverage ML to automatically design superior systems and products across the board.
He founded Vizier which is widely used across Google, including what might be the largest AI-driven culinary optimization in history - the ML Cookie experiment.
He founded Vizier which is widely used across Google, including what might be the largest AI-driven culinary optimization in history - the ML Cookie experiment.
Authored Publications
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Google
SmartChoices: Augmenting Software with Learned Implementations
Eric Yawei Chen
Νikhil Sarda
arXiv (2023)
Google Vizier: A Service for Black-Box Optimization
Subhodeep Moitra
D. Sculley
ACM (2017), pp. 1487-1495
Bayesian Optimization for a Better Dessert
Subhodeep Moitra
D. Sculley
Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, December 9, 2017, Long Beach, USA (to appear)
Black Box Optimization via a Bayesian-Optimized Genetic Algorithm
Advances in Neural Information Processing Systems 30 (NIPS 2017) (to appear)
Machine Learning: The High Interest Credit Card of Technical Debt
D. Sculley
Eugene Davydov
Todd Phillips
Dietmar Ebner
Vinay Chaudhary
Michael Young
SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)
Ad Click Prediction: a View from the Trenches
D. Sculley
Michael Young
Dietmar Ebner
Julian Grady
Lan Nie
Todd Phillips
Eugene Davydov
Sharat Chikkerur
Dan Liu
Arnar Mar Hrafnkelsson
Tom Boulos
Jeremy Kubica
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2013)
Large-Scale Learning with Less RAM via Randomization
D. Sculley
Michael Young
Proceedings of the 30 International Conference on Machine Learning (ICML) (2013), pp. 10
