13 citations · 20 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Joint Entropy Search for Multi-objective Bayesian Optimization
Ben Tu, Axel Gandy, Nikolas Kantas +1
Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi…