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Trieste: Efficiently Exploring The Depths of Black-box Functions with
  TensorFlow

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

16 February 2023
Victor Picheny
Joel Berkeley
Henry B. Moss
Hrvoje Stojić
Uri Granta
Sebastian W. Ober
A. Artemev
Khurram Ghani
Alexander Goodall
Andrei Paleyes
Sattar Vakili
Sergio Pascual-Diaz
Stratis Markou
Jixiang Qing
Nasrulloh Loka
Ivo Couckuyt
ArXivPDFHTML

Papers citing "Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow"

10 / 10 papers shown
Title
Surrogate-based optimization of system architectures subject to hidden constraints
Surrogate-based optimization of system architectures subject to hidden constraints
J. Bussemaker
P. Saves
N. Bartoli
T. Lefebvre
Björn Nagel
AI4CE
148
2
0
11 Apr 2025
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Sebastian Ament
Sam Daulton
David Eriksson
Maximilian Balandat
E. Bakshy
59
70
0
08 Jan 2025
A survey and benchmark of high-dimensional Bayesian optimization of
  discrete sequences
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Miguel González Duque
Richard Michael
Simon Bartels
Yevgen Zainchkovskyy
Søren Hauberg
Wouter Boomsma
44
4
0
07 Jun 2024
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity
  Constraints
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
Arpan Losalka
Jonathan Scarlett
29
2
0
05 Jun 2024
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Jixiang Qing
Becky D Langdon
Robert M. Lee
B. Shafei
Mark van der Wilk
Calvin Tsay
Ruth Misener
37
1
0
04 Jun 2024
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Stopping Bayesian Optimization with Probabilistic Regret Bounds
James T. Wilson
41
4
0
26 Feb 2024
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and
  Mixed Variables Gaussian Processes
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
P. Saves
R. Lafage
N. Bartoli
Y. Diouane
J. Bussemaker
T. Lefebvre
John T. Hwang
J. Morlier
J. Martins
MoE
13
61
0
23 May 2023
Unleashing the Potential of Acquisition Functions in High-Dimensional
  Bayesian Optimization
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization
Jiayu Zhao
Renyu Yang
Shenghao Qiu
Zheng Wang
13
4
0
16 Feb 2023
Benefits of Monotonicity in Safe Exploration with Gaussian Processes
Benefits of Monotonicity in Safe Exploration with Gaussian Processes
Arpan Losalka
Jonathan Scarlett
16
1
0
03 Nov 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,661
0
05 Dec 2016
1