21 citations · 22 across the 6 of their papers we have counts for
6 papers
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
Jixiang Qing, Henry Moss, Matthias Sachs
We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distribution induced by the function its…
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…
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…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
Jixiang Qing, Henry B. Moss, Tom Dhaene +1
We present Parallel Feasible Pareto Frontier Entropy Search (ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization su…
A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty
J. Qing, I. Couckuyt, T. Dhaene
Bayesian optimization is a popular tool for data-efficient optimization of expensive objective functions. In real-life applications like engineering design, the designer often want…