Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. Despite the relative importance of this step, most algorithms employ sampling- or gradient-based methods, which do not provably converge to global optima. This work investigates mixed-integer programming (MIP) as a paradigm for \emph{global} acquisition function optimization. Specifically, our Piecewise-linear Kernel Mixed Integer Quadratic Programming (PK-MIQP) formulation introduces a piecewise-linear approximation for Gaussian process kernels and admits a corresponding MIQP representation for acquisition functions. The proposed method is applicable to uncertainty-based acquisition functions for any stationary or dot-product kernel. We analyze the theoretical regret bounds of the proposed approximation, and empirically demonstrate the framework on synthetic functions, constrained benchmarks, and a hyperparameter tuning task.

Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to the machine learning community as they tend to require cleaning, thorough understanding of the chemistry, or are simply not available. In this paper, we introduce a novel dataset for yield prediction, providing the first-ever transient flow dataset for machine learning benchmarking, covering over 1200 process conditions. While previous datasets focus on discrete parameters, our experimental set-up allow us to sample a large number of continuous process conditions, generating new challenges for machine learning models. We focus on solvent selection, a task that is particularly difficult to model theoretically and therefore ripe for machine learning applications. We showcase benchmarking for regression algorithms, transfer-learning approaches, feature engineering, and active learning, with important applications towards solvent replacement and sustainable manufacturing.

Airborne particulate matter with a diameter less than 2.5 micrometers (PM2.5) is one of the most harmful air pollutants, because PM2.5 can be inhaled into human body and cause serious health problems by transmitting hazardous chemicals deeply into lung and bloodstream. A reliable, easily accessible, and low-cost PM2.5 monitoring system can greatly help people raise public awareness of PM2.5 and reduce health hazards of air pollution. In this paper, we combine image and weather information to estimate PM2.5 indices of outdoor images using deep learning and support vector regression (SVR) techniques. The proposed method first uses a convolutional neural network (CNN) to predict the PM2.5 index based on image information, and then the PM2.5 predicted by CNN and two weather features, humidity and wind speed, are combined to yield final estimated PM2.5 index using a created SVR model. We assessed our method using two datasets collected from Shanghai City and Beijing City in China and experimental results demonstrated the effectiveness of the proposed method for PM2.5 estimation.