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David Eriksson
Research Scientist - Core Data Science
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ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
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
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Latest Publications
UAI - July 28, 2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger
UAI - July 23, 2021
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
David Eriksson, Martin Jankowiak
ICML - July 18, 2021
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat
NeurIPS - July 16, 2021
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
AISTATS - July 9, 2021
Scalable Constrained Bayesian Optimization
David Eriksson, Matthias Poloczek
Journal of Machine Learning Research (JMLR) - July 1, 2021
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
Latest News
ARTIFICIAL INTELLIGENCE
July 23, 2021
High-dimensional Bayesian optimization with sparsity-inducing priors
ARTIFICIAL INTELLIGENCE
July 14, 2021
Optimizing model accuracy and latency using Bayesian multi-objective neural architecture search
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