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 …

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 …

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

End-to-end AutoML has attracted intensive interests from both academia and industry which
automatically searches for ML pipelines in a space induced by feature engineering …

Deep learning is an obvious method for the detection of disease, analyzing medical images
and many researchers have looked into it. However, the performance of deep learning …

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

The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …

Modern configurable systems provide tremendous opportunities for engineering future
intelligent software systems. A key difficulty thereof is how to effectively self-adapt the …

Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, experimental design, and database knob tuning. However, users still face …