Watch a 35-minute AutoML Seminar presentation exploring how vanilla Bayesian optimization (BO) performs effectively in high-dimensional problems. Learn why the perceived limitations of BO in high dimensions may be primarily due to assumptions about objective complexity rather than inherent algorithmic limitations. Discover how a simple modification - scaling the Gaussian process lengthscale prior with dimensionality - enables standard BO to outperform specialized algorithms on tasks with thousands of dimensions. Follow along as speaker Carl Hvarfner challenges common beliefs about BO's capabilities and demonstrates how existing high-dimensional BO approaches can be viewed through the lens of model complexity. Gain insights into how adjusting complexity assumptions can dramatically improve optimization performance without requiring complex algorithmic modifications.
Vanilla Bayesian Optimization Performs Great in High Dimensions
Overview
Syllabus
Vanilla Bayesian Optimization Performs Great in High Dimensions
Taught by
AutoML Seminars
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