References in zbMATH (referenced in 122 articles )

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  1. Boute, Robert N.; Gijsbrechts, Joren; van Jaarsveld, Willem; Vanvuchelen, Nathalie: Deep reinforcement learning for inventory control: a roadmap (2022)
  2. Cai, HanQin; McKenzie, Daniel; Yin, Wotao; Zhang, Zhenliang: Zeroth-order regularized optimization (ZORO): approximately sparse gradients and adaptive sampling (2022)
  3. Cartis, Coralia; Roberts, Lindon; Sheridan-Methven, Oliver: Escaping local minima with local derivative-free methods: a numerical investigation (2022)
  4. Chawshin, Kurdistan; Berg, Carl Fredrik; Varagnolo, Damiano; Lopez, Olivier: Automated porosity estimation using CT-scans of extracted core data (2022)
  5. Cowen-Rivers, Alexander I.; Lyu, Wenlong; Tutunov, Rasul; Wang, Zhi; Grosnit, Antoine; Griffiths, Ryan Rhys; Maraval, Alexandre Max; Jianye, Hao; Wang, Jun; Peters, Jan; Bou-Ammar, Haitham: \textttHEBO: Pushing the limits of sample-efficient hyper-parameter optimisation (2022)
  6. Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio: Symbolic DNN-tuner (2022)
  7. Hayashi, Shogo; Honda, Junya; Kashima, Hisashi: Bayesian optimization with partially specified queries (2022)
  8. Hertel, Lars; Baldi, Pierre; Gillen, Daniel L.: Reproducible hyperparameter optimization (2022)
  9. Meng, Qun; Wang, Songhao; Ng, Szu Hui: Combined global and local search for optimization with Gaussian process models (2022)
  10. Oh, Sehyeok; Lee, Seungcheol; Son, Myeonggyun; Kim, Jooha; Ki, Hyungson: Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (2022)
  11. Ozaki, Yoshihiko; Tanigaki, Yuki; Watanabe, Shuhei; Nomura, Masahiro; Onishi, Masaki: Multiobjective tree-structured Parzen estimator (2022)
  12. Pourmohamad, Tony; Lee, Herbert K. H.: Bayesian optimization via barrier functions (2022)
  13. Toscano-Palmerin, Saul; Frazier, Peter I.: Bayesian optimization with expensive integrands (2022)
  14. Vinod, Abraham P.; Israel, Arie; Topcu, Ufuk: Constrained, global optimization of unknown functions with Lipschitz continuous gradients (2022)
  15. Wang, Qihan; Feng, Yuan; Wu, Di; Li, Guoyin; Liu, Zhenyu; Gao, Wei: Polymorphic uncertainty quantification for engineering structures via a hyperplane modelling technique (2022)
  16. Xiao, Tesi; Balasubramanian, Krishnakumar; Ghadimi, Saeed: Improved complexities for stochastic conditional gradient methods under interpolation-like conditions (2022)
  17. Zhang, Haonan; Liu, Longjun; Zhou, Hengyi; Sun, Hongbin; Zheng, Nanning: CMD: controllable matrix decomposition with global optimization for deep neural network compression (2022)
  18. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (2021)
  19. Belakaria, Syrine; Deshwal, Aryan; Doppa, Janardhan Rao: Output space entropy search framework for multi-objective Bayesian optimization (2021)
  20. Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)

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