[图书][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
[HTML][HTML] A survey on multi-objective hyperparameter optimization algorithms for machine learning
A Morales-Hernández, I Van Nieuwenhuyse… - Artificial Intelligence …, 2023 - Springer
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …
performance of Machine Learning (ML) algorithms. Several methods have been developed …
Constrained Bayesian optimization with noisy experiments
Constrained Bayesian Optimization with Noisy Experiments Page 1 Bayesian Analysis (2019)
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …
Analyzing stochastic computer models: A review with opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Page 1 Statistical
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …
Active learning for deep Gaussian process surrogates
Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …
Risk-averse heteroscedastic bayesian optimization
A Makarova, I Usmanova… - Advances in Neural …, 2021 - proceedings.neurips.cc
Many black-box optimization tasks arising in high-stakes applications require risk-averse
decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the …
decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the …
Vecchia-approximated deep Gaussian processes for computer experiments
Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
[图书][B] System-and data-driven methods and algorithms
An increasing complexity of models used to predict real-world systems leads to the need for
algorithms to replace complex models with far simpler ones, while preserving the accuracy …
algorithms to replace complex models with far simpler ones, while preserving the accuracy …
Modeling the machine learning multiverse
Amid mounting concern about the reliability and credibility of machine learning research, we
present a principled framework for making robust and generalizable claims: the multiverse …
present a principled framework for making robust and generalizable claims: the multiverse …
The importance of uncertainty quantification in model reproducibility
V Volodina, P Challenor - Philosophical Transactions of …, 2021 - royalsocietypublishing.org
Many computer models possess high-dimensional input spaces and substantial
computational time to produce a single model evaluation. Although such models are often …
computational time to produce a single model evaluation. Although such models are often …