[49] Trent W Lukaczyk, Paul Constantine, Francisco Palacios, and Juan J Alonso. Active subspaces for shape optimization. In 10th AIAA multidisciplinary design optimization conference, page 1171, 2014.
[50] Matthias Mayr, Faseeh Ahmad, Konstantinos I. Chatzilygeroudis, Luigi Nardi, and Volker Krüger. Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration. CoRR, abs/2203.10033, 2022.
[51] Luigi Nardi, David Koeplinger, and Kunle Olukotun. Practical design space exploration. In 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pages 347-358, 2019.
[52] Amin Nayebi, Alexander Munteanu, and Matthias Poloczek. A framework for Bayesian Optimization in Embedded Subspaces. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research (PMLR), pages 4752-4761, 09-15 Jun 2019.
[53] Diana M. Negoescu, Peter I. Frazier, and Warren B. Powell. The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery. INFORMS Journal on Computing, 23 (3):346-363, 2011.
[54] Kai Wang Ng, Guo-Liang Tian, and Man-Lai Tang. Dirichlet and related distributions: Theory, methods and applications. 2011.
[55] Shanghai Nie and Hareton Leung. A survey of combinatorial testing. ACM Computing Surveys (CSUR), 43(2):1-29, 2011.
[56] Changyong Oh, Jakub Tomczak, Efstratios Gavves, and Max Welling. Combinatorial Bayesian Optimization using the Graph Cartesian Product. Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.
[57] Steve O'Hagan, Warwick B Dunn, Marie Brown, Joshua D Knowles, and Douglas B Kell. Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Analytical Chemistry, 77(1):290-303, 2005.
[58] Tom Packebusch and Stephan Mertens. Low autocorrelation binary sequences. Journal of Physics A: Mathematical and Theoretical, 49(16):165001, 2016.
[59] Daniel Packwood. Bayesian Optimization for Materials Science. Springer, 2017.
[60] Leonard Papenmeier, Luigi Nardi, and Matthias Poloczek. Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces. In Advances in Neural Information Processing Systems (NeurIPS), volume 35, 2022.
[61] Matthias Poloczek and David P Williamson. An experimental evaluation of fast approximation algorithms for the maximum satisfiability problem. Journal of Experimental Algorithmics (JEA), 22:1-18, 2017.
[62] Matthias Poloczek, Jialei Wang, and Peter Frazier. Multi-information source optimization. Advances in neural information processing systems, 30, 2017.
[63] Warren B Powell. A unified framework for stochastic optimization. European Journal of Operational Research, 275(3):795-821, 2019.
[64] Akshara Rai, Rika Antonova, Seungmoon Song, William Martin, Hartmut Geyer, and Christopher Atkeson. Bayesian optimization using domain knowledge on the ATRIAS biped. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1771-1778, 2018.
[65] Rommel G Regis and Christine A Shoemaker. Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Engineering Optimization, 45(5):529-555, 2013.