6 citations · 15 across the 15 of their papers we have counts for
17 papers
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Maksym Tretiakov, Sarah Filippi, Sarah Lucie Filipp +4
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistem…
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6
The optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations across batches, whose trajectories are difficult to mo…
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…
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
Toby Boyne, Jose Pablo Folch, Robert M Lee +2
We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior…
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…
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…
Gaussian Processes for Monitoring Air-Quality in Kampala
Clara Stoddart, Lauren Shrack, Richard Sserunjogi +6
Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in lo…