Jasper Snoek, Hugo Larochelle, Ryan Adams: Practical Bayesian Optimization of Machine Learning Algorithms. In: Advances in Neural Information Processing Systems. 2012, arxiv:1206.2944, bibcode:2012arXiv1206.2944S (nips.cc [PDF]).
Chris Thornton, Frank Hutter, Holger Hoos: Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Knowledge Discovery and Data Mining. 2013, arxiv:1208.3719, bibcode:2012arXiv1208.3719T (ubc.ca [PDF]).
Dougal Maclaurin, David Duvenaud, Ryan P. Adams: Gradient-based Hyperparameter Optimization through Reversible Learning. 2015, arxiv:1502.03492 [stat.ML].
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, Babak Hodjat: Evolving Deep Neural Networks. 2017, arxiv:1703.00548 [cs.NE].
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu: Population Based Training of Neural Networks. 2017, arxiv:1711.09846 [cs.LG].
Frank Hutter, Holger Hoos, Kevin Layton-Brown: Sequential model-based optimization for general algorithm configuration. In: Learning and Intelligent Optimization (Hrsg.): Lecture Notes in Computer Science. Band6683. Springer, Berlin, Heidelberg 2011, ISBN 978-3-642-25565-6, S.507–523, doi:10.1007/978-3-642-25566-3_40 (ubc.ca [PDF]).
harvard.edu
ui.adsabs.harvard.edu
Jasper Snoek, Hugo Larochelle, Ryan Adams: Practical Bayesian Optimization of Machine Learning Algorithms. In: Advances in Neural Information Processing Systems. 2012, arxiv:1206.2944, bibcode:2012arXiv1206.2944S (nips.cc [PDF]).
Chris Thornton, Frank Hutter, Holger Hoos: Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Knowledge Discovery and Data Mining. 2013, arxiv:1208.3719, bibcode:2012arXiv1208.3719T (ubc.ca [PDF]).
mit.edu
jmlr.csail.mit.edu
James Bergstra, Yoshua Bengio: Random Search for Hyper-Parameter Optimization. In: Journal of Machine Learning Research. 13. Jahrgang, 2012, S.281–305 (mit.edu [PDF]).
James Bergstra, Remi Bardenet, Yoshua Bengio, Balazs Kegl: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems. 2011 (nips.cc [PDF]).
Jasper Snoek, Hugo Larochelle, Ryan Adams: Practical Bayesian Optimization of Machine Learning Algorithms. In: Advances in Neural Information Processing Systems. 2012, arxiv:1206.2944, bibcode:2012arXiv1206.2944S (nips.cc [PDF]).
Justin Domke: Generic Methods for Optimization-Based Modeling. In: Aistats. 22. Jahrgang, 2012 (jmlr.org (Memento des Originals vom 24. Januar 2014 im Internet Archive) [abgerufen am 9. Dezember 2017]).
ubc.ca
cs.ubc.ca
Frank Hutter, Holger Hoos, Kevin Layton-Brown: Sequential model-based optimization for general algorithm configuration. In: Learning and Intelligent Optimization (Hrsg.): Lecture Notes in Computer Science. Band6683. Springer, Berlin, Heidelberg 2011, ISBN 978-3-642-25565-6, S.507–523, doi:10.1007/978-3-642-25566-3_40 (ubc.ca [PDF]).
Chris Thornton, Frank Hutter, Holger Hoos: Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Knowledge Discovery and Data Mining. 2013, arxiv:1208.3719, bibcode:2012arXiv1208.3719T (ubc.ca [PDF]).
web.archive.org
Justin Domke: Generic Methods for Optimization-Based Modeling. In: Aistats. 22. Jahrgang, 2012 (jmlr.org (Memento des Originals vom 24. Januar 2014 im Internet Archive) [abgerufen am 9. Dezember 2017]).
