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

Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in
computing power increasingly rely on parallelization rather than faster processors. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Database management system (DBMS) configuration tuning is an essential aspect of any
data-intensive application effort. But this is historically a difficult task because DBMSs have …

We study the stochastic contextual bandit problem, where the reward is generated from an
unknown function with additive noise. No assumption is made about the reward function …

Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …

Bayesian optimization is an effective methodology for the global optimization of functions
with expensive evaluations. It relies on querying a distribution over functions defined by a …

We consider the stochastic bandit problem with a continuous set of arms, with the expected
reward function over the arms assumed to be fixed but unknown. We provide two new …

Abstract Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a compelling …

Bayesian optimization has recently been proposed as a framework for automatically tuning
the hyperparameters of machine learning models and has been shown to yield state-of-the …