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 …

Abstract AutoML (Automated Machine Learning) is an emerging field that aims to automate
the process of building machine learning models. AutoML emerged to increase productivity …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, engineering, physics, and experimental design. However, it remains a …

Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications …

Recently, using automatic configuration tuning to improve the performance of modern
database management systems (DBMSs) has attracted increasing interest from the …

Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications. The …

Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …

Designing neural architectures requires immense manual efforts. This has promoted the
development of neural architecture search (NAS) to automate the design. While previous …