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With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Harold J Kushner · 1964
Earlier work this paper cites.
Single-step bayesian search method for an extremum of functions of a single variable
AG Zhilinskas · 1975
Earlier work this paper cites.
The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
Earlier work this paper cites.
Optimization by simulated annealing
Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi · 1983
Earlier work this paper cites.
Designing neural networks using genetic algorithms
Geoffrey F Miller, Peter M Todd, and Shailesh U Hegde · 1989
Earlier work this paper cites.
Handbook of genetic algorithms
Lawrence Davis · 1991
Earlier work this paper cites.
Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence
John Henry Holland et al · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
An evolutionary algorithm that constructs recurrent neural networks
Peter J Angeline, Gregory M Saunders, and Jordan B Pollack · 1994
Earlier work this paper cites.
Learning internal representations
Jonathan Baxter · 1995
Earlier work this paper cites.
Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
Earlier work this paper cites.
Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Boa: The bayesian optimization algorithm
Martin Pelikan, David E Goldberg, and Erick Cantú-Paz · 1999
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
An evaluation of landmarking variants
Johannes Fürnkranz and Johann Petrak · 2001
Earlier work this paper cites.
Combination of task description strategies and case base properties for meta-learning
Christian Köpf and Ioannis Iglezakis · 2002
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
Earlier work this paper cites.
Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results
Pavel B Brazdil, Carlos Soares, and Joaquim Pinto Da Costa · 2003
Earlier work this paper cites.
Selection of time series forecasting models based on performance information
Patrícia Maforte dos Santos, Teresa Bernarda Ludermir, and Ricardo Bastos Cavalcante Prudencio · 2004
Earlier work this paper cites.
A meta-learning method to select the kernel width in support vector regression
Carlos Soares, Pavel B Brazdil, and Petr Kuba · 2004
Earlier work this paper cites.
A cost-based model and effective heuristic for repairing constraints by value modification
Philip Bohannon, Wenfei Fan, Michael Flaster, and Rajeev Rastogi · 2005
Earlier work this paper cites.
Metalearning: Applications to data mining
Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, and Ricardo Vilalta · 2008
Earlier work this paper cites.
Metalearning-a tutorial
Christophe Giraud-Carrier · 2008
Earlier work this paper cites.
Predicting the performance of learning algorithms using support vector machines as meta-regressors
Silvio B Guerra, Ricardo BC Prudêncio, and Teresa B Ludermir · 2008
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio et al · 2009
Earlier work this paper cites.
On approximating optimum repairs for functional dependency violations
Solmaz Kolahi and Laks V. S. Lakshmanan · 2009
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
Earlier work this paper cites.
Mining multi-label data
Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
Earlier work this paper cites.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Earlier work this paper cites.
Software crisis 2.0
Brian Fitzgerald · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Provably convergent multifidelity optimization algorithm not requiring high-fidelity derivatives
Andrew March and Karen Willcox · 2012
Earlier work this paper cites.
Optimization: algorithms and consistent approximations
Elijah Polak · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
Earlier work this paper cites.
Collaborative hyperparameter tuning
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag · 2013
Earlier work this paper cites.
Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms
James Bergstra, Dan Yamins, and David D Cox · 2013
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Daniel Cox · 2013
Earlier work this paper cites.
Improving deep neural networks for lvcsr using rectified linear units and dropout
George E Dahl, Tara N Sainath, and Geoffrey E Hinton · 2013
Earlier work this paper cites.
Towards an empirical foundation for assessing bayesian optimization of hyperparameters
Katharina Eggensperger, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger Hoos, and Kevin Leyton-Brown · 2013
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Mlbase: A distributed machine-learning system
Tim Kraska, Ameet Talwalkar, John C Duchi, Rean Griffith, Michael J Franklin, and Michael I Jordan · 2013
Cited alongside, same era.
Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
Cited alongside, same era.
Datahub: Collaborative data science & dataset version management at scale
Anant P. Bhardwaj, Souvik Bhattacherjee, Amit Chavan, Amol Deshpande, Aaron J. Elmore, Samuel Madden, and Aditya G. Parameswaran · 2014
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Cited alongside, same era.
Ground: A data context service
Joseph M. Hellerstein, Vikram Sreekanti, Joseph E. Gonzalez, James Dalton, Akon Dey, Sreyashi Nag, Krishna Ramachandran, Sudhanshu Arora, Arka Bhattacharyya, Shirshanka Das, Mark Donsky, Gabriel Fierro, Chang She, Carl Steinbach, Venkat Subramanian, and Eric Sun · 2017
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Train longer, generalize better: Closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2017
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Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown · 2017
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Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
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On the state of the art of evaluation in neural language models
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Training restricted boltzmann machines
Asja Fischer and Christian Igel · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn
Brent Komer, James Bergstra, and Chris Eliasmith · 2014
Cited alongside, same era.
Bayesopt: A bayesian optimization library for nonlinear optimization, experimental design and bandits
Ruben Martinez-Cantin · 2014
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Automatic classifier selection for non-experts
Matthias Reif, Faisal Shafait, Markus Goldstein, Thomas Breuel, and Andreas Dengel · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 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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Gábor Melis, Chris Dyer, and Phil Blunsom · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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Design and analysis of experiments
Douglas C Montgomery · 2017
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Multiple adaptive bayesian linear regression for scalable bayesian optimization with warm start
Valerio Perrone, Rodolphe Jenatton, Matthias Seeger, and Cedric Archambeau · 2017
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To tune or not to tune the number of trees in random forest
Philipp Probst and Anne-Laure Boulesteix · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Atm: A distributed, collaborative, scalable system for automated machine learning
Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, and Kalyan Veeramachaneni · 2017
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Controlling false discoveries during interactive data exploration
Zheguang Zhao, Lorenzo De Stefani, Emanuel Zgraggen, Carsten Binnig, Eli Upfal, and Tim Kraska · 2017
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Handbook of big data technologies
Albert Y Zomaya and Sherif Sakr · 2017
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Maskconnect: Connectivity learning by gradient descent
Karim Ahmed and Lorenzo Torresani · 2018
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Autostacker: A compositional evolutionary learning system
Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, and Hod Lipson · 2018
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Automated selection and configuration of multi-label classification algorithms with grammar-based genetic programming
Alex GC de Sá, Alex A Freitas, and Gisele L Pappa · 2018
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Alphad3m: Machine learning pipeline synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2018
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Efficient neural architecture search with network morphism
Haifeng Jin, Qingquan Song, and Xia Hu · 2018
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Neural architecture search with bayesian optimisation and optimal transport, 2018
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric Xing · 2018
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Towards reproducible neural architecture and hyperparameter search
Aaron Klein, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Nsga-net: a multi-objective genetic algorithm for neural architecture search
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf · 2018
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
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Ml-plan: Automated machine learning via hierarchical planning
Felix Mohr, Marcel Wever, and Eyke Hüllermeier · 2018
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Predicting hyperparameters from meta-features in binary classification problems
Eleni Nisioti, K Chatzidimitriou, and A Symeonidis · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2018
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Differentiable neural network architecture search
Richard Shin, Charles Packer, and Dawn Song · 2018
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Joaquin Vanschoren · 2018
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Rafiki: Machine learning as an analytics service system
Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, and Beng Chin Ooi · 2018
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Scalable gaussian process-based transfer surrogates for hyperparameter optimization
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Multi-fidelity automatic hyper-parameter tuning via transfer series expansion
Yi-Qi Hu, Yang Yu, Wei-Wei Tu, Qiang Yang, Yuqiang Chen, and Wenyuan Dai · 2019
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Random search and reproducibility for neural architecture search, 2019
Liam Li and Ameet Talwalkar · 2019
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Evolutionary neural automl for deep learning, 2019
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, and Risto Miikkulainen · 2019
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Smartml: A meta learning-based framework for automated selection and hyperparameter tuning for machine learning algorithms
Mohamed Maher and Sherif Sakr · 2019
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Evolving deep neural networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, et al · 2019
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Encyclopedia of Big Data Technologies
Sherif Sakr and Albert Y. Zomaya, editors · 2019
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Democratizing data science through interactive curation of ml pipelines
Zeyuan Shang, Emanuel Zgraggen, Benedetto Buratti, Ferdinand Kossmann, Yeounoh Chung, Philipp Eichmann, Carsten Binnig, Eli Upfal, and Tim Kraska · 2019
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OBOE: Collaborative filtering for AutoML initialization
Chengrun Yang, Yuji Akimoto, Dae Won Kim, and Madeleine Udell · 2019
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