References in zbMATH (referenced in 26 articles )

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  1. 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)
  2. Järvenpää, Marko; Gutmann, Michael U.; Pleska, Arijus; Vehtari, Aki; Marttinen, Pekka: Efficient acquisition rules for model-based approximate Bayesian computation (2019)
  3. Letham, Benjamin; Karrer, Brian; Ottoni, Guilherme; Bakshy, Eytan: Constrained Bayesian optimization with noisy experiments (2019)
  4. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  5. Candelieri, A.; Perego, R.; Archetti, F.: Bayesian optimization of pump operations in water distribution systems (2018)
  6. Chan, Shing; Elsheikh, Ahmed H.: A machine learning approach for efficient uncertainty quantification using multiscale methods (2018)
  7. Durand, Audrey; Maillard, Odalric-Ambrym; Pineau, Joelle: Streaming kernel regression with provably adaptive mean, variance, and regularization (2018)
  8. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  9. Järvenpää, Marko; Gutmann, Michael U.; Vehtari, Aki; Marttinen, Pekka: Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria (2018)
  10. Kailkhura, Bhavya; Thiagarajan, Jayaraman J.; Rastogi, Charvi; Varshney, Pramod K.; Bremer, Peer-Timo: A spectral approach for the design of experiments: design, analysis and algorithms (2018)
  11. Li, Lisha; Jamieson, Kevin; DeSalvo, Giulia; Rostamizadeh, Afshin; Talwalkar, Ameet: Hyperband: a novel bandit-based approach to hyperparameter optimization (2018)
  12. Mai, Feng; Fry, Michael J.; Ohlmann, Jeffrey W.: Model-based capacitated clustering with posterior regularization (2018)
  13. Soize, C.: Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm (2018)
  14. Tsamardinos, Ioannis; Greasidou, Elissavet; Borboudakis, Giorgos: Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation (2018)
  15. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  16. Andersen, Michael Riis; Vehtari, Aki; Winther, Ole; Hansen, Lars Kai: Bayesian inference for spatio-temporal spike-and-slab priors (2017)
  17. Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017) arXiv
  18. Bui, Thang D.; Yan, Josiah; Turner, Richard E.: A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation (2017)
  19. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  20. Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.: Deep learning in color: towards automated quark/gluon jet discrimination (2017)

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