Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can
substantially impact their performance. To support users in determining well-performing …
substantially impact their performance. To support users in determining well-performing …
Unexpected improvements to expected improvement for bayesian optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian
optimization and has found countless successful applications, but its performance is often …
optimization and has found countless successful applications, but its performance is often …
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …
applications, including automatic machine learning, engineering, physics, and experimental …
Automated self-optimization, intensification, and scale-up of photocatalysis in flow
The optimization, intensification, and scale-up of photochemical processes constitute a
particular challenge in a manufacturing environment geared primarily toward thermal …
particular challenge in a manufacturing environment geared primarily toward thermal …
SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
Optimizing millions of hyperparameters by implicit differentiation
We propose an algorithm for inexpensive gradient-based hyperparameter optimization that
combines the implicit function theorem (IFT) with efficient inverse Hessian approximations …
combines the implicit function theorem (IFT) with efficient inverse Hessian approximations …
Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling
Develo** high-energy and efficient battery technologies is a crucial aspect of advancing
the electrification of transportation and aviation. However, battery innovations can take years …
the electrification of transportation and aviation. However, battery innovations can take years …