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 …

Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

Abstract We present Syne Tune, a library for large-scale distributed hyperparameter
optimization (HPO). Syne Tune's modular architecture allows users to easily switch between …

We present Amazon SageMaker Autopilot: a fully managed system that provides an
automatic machine learning solution. Given a tabular dataset and the target column name …

Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-
box manner. To handle practical settings where gathering data requires use of finite …

Many real-world optimization scenarios involve expensive evaluation with unknown and
heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution …

The past few years have seen the development of many benchmarks for Neural Architecture
Search (NAS), fueling rapid progress in NAS research. However, recent work, which shows …

Building energy management usually involves a number of objectives, such as investment
costs, thermal comfort, system resilience, battery life, and many others. However, most …

Bayesian optimization is a sample efficient sequential global optimization method for black-
box, expensive and multi-extremal functions. It generates, and keeps updated, a …

Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-
box functions. BO budgets are typically given in iterations, which implicitly assumes each …