Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …

We propose information-directed sampling--a new algorithm for online optimization
problems in which a decision-maker must balance between exploration and exploitation …

How can we efficiently gather information to optimize an unknown function, when presented
with multiple, mutually dependent information sources with different costs? For example …

We propose information-directed sampling—a new approach to online optimization
problems in which a decision maker must balance between exploration and exploitation …

Bayesian optimization (BO) has become a popular strategy for global optimization of
expensive real-world functions. Contrary to a common expectation that BO is suited to …

Big data applications are typically associated with systems involving large numbers of users,
massive complex software systems, and large-scale heterogeneous computing and storage …

Computer simulators can be computationally intensive to run over a large number of input
values, as required for optimization and various uncertainty quantification tasks. The …

This paper proposes a machine learning–based multifidelity modeling (MFM) and
information-theoretic Bayesian optimization approach where the associated models can …

This paper explores the use of autonomous underwater vehicles (AUVs) equipped with
sensors to construct water quality models to aid in the assessment of important …