2 citations · 6 across the 8 of their papers we have counts for
9 papers
Beyond adjacency: Graph encoding with reachability and shortest paths
Shiqiang Zhang, Ruth Misener
Graph-structured data is central to many scientific and industrial domains, where the goal is often to optimize objectives defined over graph structures. Given the combinatorial co…
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…
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…
Limeade: Let integer molecular encoding aid
Shiqiang Zhang, Christian W. Feldmann, Frederik Sandfort +3
Mixed-integer programming (MIP) is a well-established framework for computer-aided molecular design (CAMD). By precisely encoding the molecular space and score functions, e.g., a g…
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…
Verifying message-passing neural networks via topology-based bounds tightening
Christopher Hojny, Shiqiang Zhang, Juan S. Campos +1
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust…
Augmenting optimization-based molecular design with graph neural networks
Shiqiang Zhang, Juan S. Campos, Christian Feldmann +3
Computer-aided molecular design (CAMD) studies quantitative structure-property relationships and discovers desired molecules using optimization algorithms. With the emergence of ma…
Practical Path-based Bayesian Optimization
Jose Pablo Folch, James Odgers, Shiqiang Zhang +6
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be…