Constrained Bayesian Optimization with Noisy Experiments

Bayesian Analysis 2018


Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its applicability to many randomized experiments. We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized. Simulations with synthetic functions show that optimization performance on noisy, constrained problems outperforms existing methods. We further demonstrate the effectiveness of the method with two real-world experiments conducted at Facebook: optimizing a ranking system, and optimizing server compiler flags.

Related Publications

All Publications

Efficient tuning of online systems using Bayesian optimization

Ben Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy

September 17, 2018

An Exploration of Embodied Visual Exploration

Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman

arXiv - August 21, 2020

Encoding Physical Constraints in Differentiable Newton-Euler Algorithm

Giovanni Sutanto, Austin S. Wang, Yixin Lin, Mustafa Mukadam, Gaurav S. Sukhatme, Akshara Rai, Franziska Meier

L4DC - June 10, 2020

Country Differences in Social Comparison on Social Media

Justin Cheng, Moira Burke, Bethany de Gant

CSCW - October 17, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy