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The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Harold J. Kushner. 1964 · 1964
Earlier work this paper cites.
The application of Bayesian methods for seeking the extremum
Jonas Močkus, Vytautas Tiešis, and Antanas Žilinskas. 1978 · 1978
Earlier work this paper cites.
A simple sequentially rejective multiple test procedure
Sture Holm. 1979 · 1979
Earlier work this paper cites.
A simple sequentially rejective multiple test procedure
Sture Holm. 1979 · 1979
Earlier work this paper cites.
Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins. 1985 · 1985
Earlier work this paper cites.
A limited memory algorithm for bound constrained optimization
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu. 1995 · 1995
Earlier work this paper cites.
Computer experiments and global optimization
Matthias Schonlau. 1997 · 1997
Earlier work this paper cites.
Efficient Global Optimization of Expensive Black-Box Functions
Donald R. Jones, Matthias Schonlau, and William J. Welch. 1998 · 1998
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 1998 · 1998
Earlier work this paper cites.
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
Michael D. McKay, Richard J. Beckman, and William J. Conover. 2000 · 2000
Earlier work this paper cites.
A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2001 · 2001
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
Donald R. Jones. 2001 · 2001
Earlier work this paper cites.
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
András Sóbester, Stephen J. Leary, and Andy J. Keane. 2005 · 2005
Earlier work this paper cites.
A Tutorial on the Performance Assesment of Stochastic Multiobjective Optimizers
Joshua D. Knowles, Lothar Thiele, and Eckart Zitzler. 2006 · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams. 2006 · 2006
Earlier work this paper cites.
A Tutorial on the Performance Assesment of Stochastic Multiobjective Optimizers
Joshua D. Knowles, Lothar Thiele, and Eckart Zitzler. 2006 · 2006
Cited alongside, same era.
Engineering Design via Surrogate Modelling - A Practical Guide
Alexander I. J. Forrester, Andras Sobester, and Andy J. Keane. 2008 · 2008
Cited alongside, same era.
Practical Bayesian optimization
Daniel J. Lizotte. 2008 · 2008
Cited alongside, same era.
Gaussian process optimization in the bandit setting: no regret and experimental design. In Proceedings of the 27th International Conference on Machine Learning . Omnipress, 1015–1022
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. 2010 · 2010
Cited alongside, same era.
Adaptive ε \varepsilon -greedy exploration in reinforcement learning based on value differences. In Annual Conference on Artificial Intelligence . Springer, 203–210
Michel Tokic. 2010 · 2010
Cited alongside, same era.
A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization
Zhiwei Feng, Qingbin Zhang, Qingfu Zhang, Qiangang Tang, Tao Yang, and Yang Ma. 2015 · 2015
Later among the works it cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, et al · 2015
Later among the works it cites.
Multi-point Efficient Global Optimization Using Niching Evolution Strategy. In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI . Springer, 146–162
Hao Wang, Thomas Bäck, and Michael T. M. Emmerich. 2015 · 2015
Later among the works it cites.
GPyOpt: A Bayesian Optimization framework in Python
GPyOpt. 2016 · 2016
Later among the works it cites.
Taking the human out of the loop: A review of Bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. 2016 · 2016
Later among the works it cites.
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Convergence rates of efficient global optimization algorithms
Adam D. Bull. 2011 · 2011
Cited alongside, same era.
GPy: A Gaussian process framework in Python
GPy. since 2012 · 2012
Cited alongside, same era.
Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems . Curran Associates, Inc., 2951–2959
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
Cited alongside, same era.
On the Effect of Response Transformations in Sequential Parameter Optimization
Tobias Wagner and Simon Wessing. 2012 · 2012
Cited alongside, same era.
MOI-MBO: Multiobjective infill for parallel model-based optimization. In International Conference on Learning and Intelligent Optimization . Springer, 173–186
Bernd Bischl, Simon Wessing, Nadja Bauer, Klaus Friedrichs, and Claus Weihs. 2014 · 2014
Cited alongside, same era.
Description of adjointShapeOptimizationFoam and how to implement new objective functions
Ulf Nilsson, Daniel Lindblad, and Olivier Petit. 2014 · 2014
Cited alongside, same era.
Theoretical analysis of Bayesian optimisation with unknown Gaussian process hyper-parameters
Ziyu Wang and Nando de Freitas. 2014 · 2014
Cited alongside, same era.
Simple Intuitive Multi-objective ParalLElization of Efficient Global Optimization: SIMPLE-EGO. In World Congress of Structural and Multidisciplinary Optimisation . Springer, 205–220
Carla Grobler, Schalk Kok, and Daniel N Wilke. 2017 · 2017
Later among the works it cites.
Max-value entropy search for efficient Bayesian optimization. In Proceedings of the 34th International Conference on Machine Learning . PMLR, 3627–3635
Zi Wang and Stefanie Jegelka. 2017 · 2017
Later among the works it cites.
A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics. In Parallel Problem Solving from Nature – PPSN XV . Springer, 296–307
Steven J. Daniels, Alma A. M. Rahat, Richard M. Everson, Gavin R. Tabor, and Jonathan E. Fieldsend. 2018 · 2018
Later among the works it cites.
Active Model Learning and Diverse Action Sampling for Task and Motion Planning. In Proceedings of the International Conference on Intelligent Robots and Systems . IEEE, 4107–4114
Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. 2018 · 2018
Later among the works it cites.
Automated shape optimisation of a plane asymmetric diffuser using combined Computational Fluid Dynamic simulations and multi-objective Bayesian methodology
Steven J. Daniels, Alma A. M. Rahat, Gavin R.. Tabor, Jonathan E. Fieldsend, and Richard M. Everson. 2019 · 2019
Closest in time.
Bi-objective decision making in global optimization based on statistical models
Antanas Žilinskas and James Calvin. 2019 · 2019
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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G. Wilson, and Eytan Bakshy. 2020 · 2020
Closest in time.
BINOCULARS for efficient, nonmyopic sequential experimental design. In International Conference on Machine Learning . PMLR, 4794–4803
Shali Jiang, Henry Chai, Javier Gonzalez, and Roman Garnett. 2020 · 2020
Closest in time.
Deep reinforcement learning with double Q-learning. In Proceedings of the 13th AAAI Conference on Artificial Intelligence . AAAI Press, 2094–2100
Hado van Hasselt, Arthur Guez, and David Silver. 2016 · 2094
Closest in time.