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 …
A review of the recent progress in battery informatics
C Ling - npj Computational Materials, 2022 - nature.com
Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …
transportation in the current and future society. Recently machine learning (ML) has …
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 …
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
R Turner, D Eriksson, M McCourt… - NeurIPS 2020 …, 2021 - proceedings.mlr.press
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 …
Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling
Developing 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 …
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 …
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 …
Predicting 3D genome folding from DNA sequence with Akita
In interphase, the human genome sequence folds in three dimensions into a rich variety of
locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key …
locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key …