Robust Multi-Objective Bayesian Optimization Under Input Noise
Sam Daulton, Sait Cakmak, Max Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
I lead the Modeling & Optimization team within the Adaptive Experimentation group on Meta’s Core Data Science team. We focus on developing methods and tools for probabilistic modeling and sample-efficient optimization, and apply them to a broad range of applications across the company, including infrastructure optimization, AutoML, online A/B tests, ranking systems, and AR/VR. I also lead the development of BoTorch, an open-source library for Bayesian Optimization in PyTorch.
In the past, I have also worked on the intersection of Machine Learning and Econometrics, in particular on how to utilize Machine Learning algorithms to perform causal inference in experimental and non-experimental settings.
I hold an MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley.
Bayesian Optimization, Gaussian processes, probabilistic modeling, causal inference and machine Learning
Sam Daulton, Sait Cakmak, Max Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
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Wesley J. Maddox, Max Balandat, Andrew Gordon Wilson, Eytan Bakshy
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Sam Daulton, Max Balandat, Eytan Bakshy
Sam Daulton, Max Balandat, Eytan Bakshy