References in zbMATH (referenced in 52 articles )

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  1. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  2. Beyhaghi, Pooriya; Alimo, Ryan; Bewley, Thomas: A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging (2020)
  3. Jiang, Wei; Siddiqui, Sauleh: Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (2020)
  4. Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em: Physics-informed neural networks for high-speed flows (2020)
  5. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  6. Sirén, Jukka; Kaski, Samuel: Local dimension reduction of summary statistics for likelihood-free inference (2020)
  7. Wang, Qihan; Li, Qingya; Wu, Di; Yu, Yuguo; Tin-Loi, Francis; Ma, Juan; Gao, Wei: Machine learning aided static structural reliability analysis for functionally graded frame structures (2020)
  8. Wu, Hao; Noé, Frank: Variational approach for learning Markov processes from time series data (2020)
  9. Ariafar, Setareh; Coll-Font, Jaume; Brooks, Dana; Dy, Jennifer: ADMMBO: Bayesian optimization with unknown constraints using ADMM (2019)
  10. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  11. Candelieri, Antonio; Giordani, Ilaria; Archetti, Francesco; Barkalov, Konstantin; Meyerov, Iosif; Polovinkin, Alexey; Sysoyev, Alexander; Zolotykh, Nikolai: Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization (2019)
  12. ChangYong Oh, Efstratios Gavves, Max Welling: BOCK : Bayesian Optimization with Cylindrical Kernels (2019) arXiv
  13. Flaxman, Seth; Chirico, Michael; Pereira, Pau; Loeffler, Charles: Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “Real-time crime forecasting challenge” (2019)
  14. Järvenpää, Marko; Gutmann, Michael U.; Pleska, Arijus; Vehtari, Aki; Marttinen, Pekka: Efficient acquisition rules for model-based approximate Bayesian computation (2019)
  15. Joy, Thomas; Desmaison, Alban; Ajanthan, Thalaiyasingam; Bunel, Rudy; Salzmann, Mathieu; Kohli, Pushmeet; Torr, Philip H. S.; Kumar, M. Pawan: Efficient relaxations for dense CRFs with sparse higher-order potentials (2019)
  16. Letham, Benjamin; Karrer, Brian; Ottoni, Guilherme; Bakshy, Eytan: Constrained Bayesian optimization with noisy experiments (2019)
  17. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  18. Mariappan, Ragunathan; Rajan, Vaibhav: Deep collective matrix factorization for augmented multi-view learning (2019)
  19. Oates, C. J.; Sullivan, T. J.: A modern retrospective on probabilistic numerics (2019)
  20. Raissi, M.; Perdikaris, P.; Karniadakis, G. E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (2019)

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