1 citations · 1 across the 3 of their papers we have counts for
5 papers
BoGrape: Bayesian optimization over graphs with shortest-path encoded
Yilin Xie, Shiqiang Zhang, Jixiang Qing +2
Graph-structured data are central to many scientific and industrial applications where the goal is to optimize expensive black-box objectives defined over graph structures or node…
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Yilin Xie, Shiqiang Zhang, Joel A. Paulson +1
Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. D…
Adversarially Perturbed Precision Matrix Estimation
Yiling Xie
Precision matrix estimation is a fundamental topic in multivariate statistics and modern machine learning. This paper proposes an adversarially perturbed precision matrix estimatio…
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Toby Boyne, Juan S. Campos, Becky D. Langdon +11
Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemi…
Global optimization of graph acquisition functions for neural architecture search
Yilin Xie, Shiqiang Zhang, Jixiang Qing +2
Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing gr…