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Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations.
Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
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Bayesian approach to global optimization and application to multiobjective and constrained problems
J.B. Mockus and L.J. Mockus · 1991
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A new algorithm for error-tolerant subgraph isomorphism detection
Bruno T Messmer and Horst Bunke · 1998
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Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto · 1998
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Graph distances using graph union
Walter D Wallis, Peter Shoubridge, M Kraetz, and D Ray · 2001
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Diffusion kernels on graphs and other discrete input spaces
Risi Imre Kondor and John Lafferty · 2002
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Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
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Kernels and regularization on graphs
Alexander J Smola and Risi Kondor · 2003
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Gaussian Processes for Machine Learning
C.E. Rasmussen and C.K.I. Williams · 2006
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Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Optimal transport: old and new , volume 338
Cédric Villani · 2008
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
Eric Brochu, Vlad M. Cora, and Nando de Freitas · 2010
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A survey of graph edit distance
Xinbo Gao, Bing Xiao, Dacheng Tao, and Xuelong Li · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Dealing with asynchronicity in parallel gaussian process based global optimization
David Ginsbourger, Janis Janusevskis, and Rodolphe Le Riche · 2011
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2d image registration in ct images using radial image descriptors
Franz Graf, Hans-Peter Kriegel, Matthias Schubert, Sebastian Pölsterl, and Alexander Cavallaro · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Daniel Cox · 2013
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Adanet: Adaptive structural learning of artificial neural networks
Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Contextual decision processes with low bellman rank are pac-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2016
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Fast bayesian optimization of machine learning hyperparameters on large datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 2016
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Towards automatically-tuned neural networks
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter · 2016
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Feedback prediction for blogs
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Raiders of the lost architecture: Kernels for bayesian optimization in conditional parameter spaces
Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, and Michael A Osborne · 2014
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Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems
Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Joan P Avariento, Tomás J Arnau, Mauri Benedito-Bordonau, and Joaquín Huerta · 2014
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A proactive intelligent decision support system for predicting the popularity of online news
Kelwin Fernandes, Pedro Vinagre, and Paulo Cortez · 2015
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Scalable, Active and Flexible Learning on Distributions
Dougal J Sutherland · 2015
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2017
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Bayesian optimization with tree-structured dependencies
Rodolphe Jenatton, Cedric Archambeau, Javier González, and Matthias Seeger · 2017
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Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, and Barnabas Poczos · 2017
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Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2017
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Deeparchitect: Automatically designing and training deep architectures
Renato Negrinho and Geoff Gordon · 2017
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Computational Optimal Transport
Gabriel Peyré and Marco Cuturi · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc Le, and Alex Kurakin · 2017
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Lingxi Xie and Alan Yuille · 2017
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Practical network blocks design with q-learning
Zhao Zhong, Junjie Yan, and Cheng-Lin Liu · 2017
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2017
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