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Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS. 2960--2968. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS. 2960--2968."},{"unstructured":"Kihyuk Sohn Honglak Lee and Xinchen Yan. 2015. Learning Structured Output Representation using Deep Conditional Generative Models. In NIPS Corinna Cortes Neil D. Lawrence Daniel D. Lee Masashi Sugiyama and Roman Garnett (Eds.). 3483--3491.  Kihyuk Sohn Honglak Lee and Xinchen Yan. 2015. Learning Structured Output Representation using Deep Conditional Generative Models. In NIPS Corinna Cortes Neil D. Lawrence Daniel D. Lee Masashi Sugiyama and Roman Garnett (Eds.). 3483--3491.","key":"e_1_3_2_2_59_1"},{"key":"e_1_3_2_2_60_1","volume-title":"Yan qing Lu, and Ting Xu","author":"Song Maowen","year":"2022","unstructured":"Maowen Song , Lei Feng , Pengcheng Huo , Mingze Liu , Chunyu Huang , Feng Yan , Yan qing Lu, and Ting Xu . 2022 . 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Oriol Vinyals Timo Ewalds Sergey Bartunov Petko Georgiev Alexander Sasha Vezhnevets Michelle Yeo Alireza Makhzani Heinrich K\u00fcttler John P. Agapiou Julian Schrittwieser John Quan Stephen Gaffney Stig Petersen Karen Simonyan Tom Schaul Hado van Hasselt David Silver Timothy P. Lillicrap Kevin Calderone Paul Keet Anthony Brunasso David Lawrence Anders Ekermo Jacob Repp and Rodney Tsing. 2017. StarCraft II: A New Challenge for Reinforcement Learning. CoRR Vol. abs\/1708.04782 (2017).","key":"e_1_3_2_2_61_1"},{"unstructured":"Jing Wang Zhenyue Zhang and Hongyuan Zha. 2004. Adaptive Manifold Learning. In NIPS. 1473--1480.  Jing Wang Zhenyue Zhang and Hongyuan Zha. 2004. Adaptive Manifold Learning. In NIPS. 1473--1480.","key":"e_1_3_2_2_62_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_63_1","DOI":"10.1016\/j.future.2022.05.014"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_64_1","DOI":"10.1109\/TNNLS.2020.2978386"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_65_1","DOI":"10.1021\/acsphotonics.6b00066"},{"volume-title":"RID-Noise: Towards Robust Inverse Design under Noisy Environments","author":"Yang Jia-Qi","unstructured":"Jia-Qi Yang , Ke-Bin Fan , Hao Ma , and De-Chuan Zhan . 2022. RID-Noise: Towards Robust Inverse Design under Noisy Environments . In AAAI. AAAI Press , 4654--4661. Jia-Qi Yang, Ke-Bin Fan, Hao Ma, and De-Chuan Zhan. 2022. RID-Noise: Towards Robust Inverse Design under Noisy Environments. In AAAI. 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