6 citations · 15 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
BoFire: Bayesian Optimization Framework Intended for Real Experiments
Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte +15
Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Pre…
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Jixiang Qing, Becky D Langdon, Robert M Lee +4
We consider the problem of optimizing initial conditions and termination time in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating diff…
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
Ruby Sedgwick, John P. Goertz, Molly M. Stevens +2
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a sp…
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…
Weighted-Sum of Gaussian Process Latent Variable Models
James Odgers, Ruby Sedgwick, Chrysoula Kappatou +2
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Pro…
Transition Constrained Bayesian Optimization via Markov Decision Processes
Jose Pablo Folch, Calvin Tsay, Robert M Lee +6
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many rea…