Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon
I do research in machine learning with a focus on scaling Bayesian optimization and Gaussian processes to complex high-dimensional problems. I was previously a Sr. Research Scientist at Uber AI Labs and before that I received my Ph.D. in Applied Mathematics from Cornell University.
Interests
Bayesian optimization, AutoML, Gaussian processes, scientific computing
Latest Publications
Journal of Machine Learning Research (JMLR) - 2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
David Eriksson, Martin Jankowiak
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Scalable Constrained Bayesian Optimization
David Eriksson, Matthias Poloczek
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
David Eriksson, Pierce I-Jen Chuang, Sam Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Max Balandat
