ResearchTrend.AI
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

David Eriksson
D. Bindel
C. Shoemaker
ArXivPDFHTML

Papers citing "pySOT and POAP: An event-driven asynchronous framework for surrogate optimization"

5 / 5 papers shown
Title
Genetically programmable optical random neural networks
Genetically programmable optical random neural networks
Bora Çarpinlioglu
Bahrem Serhat Danis
41
5
0
19 Mar 2024
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner
David Eriksson
M. McCourt
J. Kiili
Eero Laaksonen
Zhen Xu
Isabelle M Guyon
BDL
30
289
0
20 Apr 2021
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
S. Hudson
Jeffrey Larson
John-Luke Navarro
Stefan M. Wild
16
28
0
16 Apr 2021
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander I. Cowen-Rivers
Wenlong Lyu
Rasul Tutunov
Zhi Wang
Antoine Grosnit
...
A. Maraval
Hao Jianye
Jun Wang
Jan Peters
H. Ammar
27
74
0
07 Dec 2020
On the implementation of a global optimization method for mixed-variable problems
On the implementation of a global optimization method for mixed-variable problems
G. Nannicini
16
19
0
04 Sep 2020