Recent advances in Bayesian optimization

X Wang, Y Jin, S Schmitt, M Olhofer - ACM Computing Surveys, 2023 - dl.acm.org
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Bayesian optimization for adaptive experimental design: A review

S Greenhill, S Rana, S Gupta, P Vellanki… - IEEE …, 2020 - ieeexplore.ieee.org
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

Max-value entropy search for efficient Bayesian optimization

Z Wang, S Jegelka - International Conference on Machine …, 2017 - proceedings.mlr.press
Abstract Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a compelling …

A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization

M Binois, N Wycoff - ACM Transactions on Evolutionary Learning and …, 2022 - dl.acm.org
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Perspectives on the integration between first-principles and data-driven modeling

W Bradley, J Kim, Z Kilwein, L Blakely… - Computers & Chemical …, 2022 - Elsevier
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly

K Kandasamy, KR Vysyaraju, W Neiswanger… - Journal of Machine …, 2020 - jmlr.org
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

Edge: Explaining deep reinforcement learning policies

W Guo, X Wu, U Khan, X Xing - Advances in Neural …, 2021 - proceedings.neurips.cc
With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …

High dimensional Bayesian optimisation and bandits via additive models

K Kandasamy, J Schneider… - … conference on machine …, 2015 - proceedings.mlr.press
Bayesian Optimisation (BO) is a technique used in optimising a D-dimensional function
which is typically expensive to evaluate. While there have been many successes for BO in …

Structure discovery in nonparametric regression through compositional kernel search

D Duvenaud, J Lloyd, R Grosse… - International …, 2013 - proceedings.mlr.press
Despite its importance, choosing the structural form of the kernel in nonparametric
regression remains a black art. We define a space of kernel structures which are built …

Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios

Z Chen, S Wu, S Shen, Y Liu, F Guo, Y Zhang - Energy, 2023 - Elsevier
Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric
vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to …