Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …

Bayesian optimization has shown success in global optimization of expensive-to-evaluate
multimodal objective functions. However, unlike most optimization methods, Bayesian …

Optimizing discrete black-box functions is key in several domains, eg protein engineering
and drug design. Due to the lack of gradient information and the need for sample efficiency …

Many of the recent triumphs in machine learning are dependent on well-tuned
hyperparameters. This is particularly prominent in reinforcement learning (RL) where a small …

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for
accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of …

Reinforcement learning (RL) aims to find an optimal policy by interaction with an
environment. Consequently, learning complex behavior requires a vast number of samples …

How should state be constructed from a sequence of observations, so as to best achieve
some objective? Most deep learning methods update the parameters of the state …