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

We present a tutorial on Bayesian optimization, a method of finding the maximum of
expensive cost functions. Bayesian optimization employs the Bayesian technique of setting …

In parallel with the development of information and network technology, large amounts of
data are being generated by the Internet, and data-driven methodologies are now often …

Abstract Machine learning (ML) is the fastest growing field in computer science, and health
informatics is among the greatest challenges. The goal of ML is to develop algorithms which …

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 …

We propose a novel information-theoretic approach for Bayesian optimization called
Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that …

The performance of imitation learning is typically upper-bounded by the performance of the
demonstrator. While recent empirical results demonstrate that ranked demonstrations allow …

Bayesian optimization techniques have been successfully applied to robotics, planning,
sensor placement, recommendation, advertising, intelligent user interfaces and automatic …

We present a framework for data-driven robotics that makes use of a large dataset of
recorded robot experience and scales to several tasks using learned reward functions. We …

Our goal is to accurately and efficiently learn reward functions for autonomous robots.
Current approaches to this problem include inverse reinforcement learning (IRL), which …