Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

Through condition-based maintenance strategy, engineers can monitor the health states of
equipment and take actions based on the sensor data. Limited by the low failure frequency …

We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a
novel framework for learning deep kernel Gaussian processes (GPs) by interpolating …

Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for
managing water resources and establishing early-warning systems. However, this can be a …

Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

Transferable graph learning involves knowledge transferability from a source graph to a
relevant target graph. The major challenge of transferable graph learning is the distribution …

Many real-world problems are computationally costly and the objective functions evolve over
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …

One main challenge in multi-objective Bayesian optimization of expensive problems is that
only a very limited number of fitness evaluations can be afforded. To address the above …

Transfer multiobjective optimization promises sample-efficient discovery of near Pareto-
optimal solutions to a target task by utilizing experiential priors from related source tasks. In …