625. Bayesian optimization for mixed feature spaces using tree kernels and graph kernels
Invited abstract in session TA-1: Plenary 2, stream Plenaries.
Tuesday, 9:00-10:00Room: B100/1001
Authors (first author is the speaker)
| 1. | Ruth Misener
|
| Department of Computing, Imperial College London |
Abstract
Bayesian optimization is effectively a two-step iterative process that first trains a surrogate model using continuous optimization over hyperparameter space and then optimizes the acquisition function over the search space. We investigate Bayesian optimization for mixed-feature search spaces using both tree kernels and graph kernels for Gaussian processes. With respect to trees kernels, our Bayesian Additive Regression Trees Kernel (BARK) uses tree agreement to define a posterior over sum-of-tree functions. With respect to graph kernels, our acquisition function with shortest paths encoded allows us to optimize over graphs, for instance to find the best graph structure and/or node features. We formulate both acquisition functions using mixed-integer optimization and show applications to a variety of challenges in molecular design, engineering and machine learning.
This work is joint with Toby Boyne, Alexander Thebelt, Yilin Xie, Shiqiang Zhang, Jixiang Qing, Jose Folch, Robert Lee, Nathan Sudermann-Merx, David Walz, Behrang Shafei, and Calvin Tsay.
Keywords
- Optimization for learning and data analysis
Status: accepted
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