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 …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

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 …

Faced with the challenges of big data, modern cloud database management systems are
designed to efficiently store, organize, and retrieve data, supporting optimal performance …

Distributed data analytic engines like Spark are common choices to process massive data in
industry. However, the performance of Spark SQL highly depends on the choice of …

Artificial neural networks have proved to be useful in a host of demanding applications,
therefore becoming increasingly important in science and engineering. Large-scale …

Wrist-based input often requires tuning parameter settings in correspondence to between-
user and between-session differences, such as variations in hand anatomy, wearing …

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such
cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer …

Bandit-based methods are commonly used for hyperparameter optimization (HPO), which is
significant in data analytics. When confronted with numerous configurations and high …