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Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible.
An almost optimal algorithm for unbounded searching
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A practical approach to feature selection
K. Kira and L. A. Rendell · 1992
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Irrelevant features and the subset selection problem
G. H. John, R. Kohavi, and K. Pfleger · 1994
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Bias, Variance, and Arcing Classifiers
L. Breiman · 1996
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Wrappers for feature subset selection
R. Kohavi and G. H. John · 1997
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Feature subset selection using a genetic algorithm
J. Yang and V. Honavar · 1998
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The role of occam’s razor in knowledge discovery
P. Domingos · 1999
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Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper
M. A. Hall and L. A. Smith · 1999
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Gene selection for cancer classification using support vector machines
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik · 2002
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What is a statistical model?
P. McCullagh · 2002
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Statistical models
A. C. Davison · 2003
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Theoretical and empirical analysis of relieff and rrelieff
M. Robnik-Šikonja and I. Kononenko · 2003
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Toward integrating feature selection algorithms for classification and clustering
H. Liu and L. Yu · 2005
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
E. J. Candès, J. Romberg, and T. Tao · 2006
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Table extraction using spatial reasoning on the css2 visual box model
W. Gatterbauer and P. Bohunsky · 2006
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Sparse multinomial logistic regression via bayesian l1 regularisation
G. C. Cawley, N. L. Talbot, and M. Girolami · 2007
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Iterative relief for feature weighting: algorithms, theories, and applications
Y. Sun · 2007
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Data integration for the relational web
M. J. Cafarella, A. Halevy, and N. Khoussainova · 2009
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Exact matrix completion via convex optimization
E. J. Candès and B. Recht · 2009
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Normalized mutual information feature selection
P. A. Estévez, M. Tesmer, C. A. Perez, and J. M. Zurada · 2009
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Matrix completion with noise
E. J. Candes and Y. Plan · 2010
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Google fusion tables: data management, integration and collaboration in the cloud
H. Gonzalez, A. Halevy, C. S. Jensen, A. Langen, J. Madhavan, R. Shapley, and W. Shen · 2010
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Feature selection: An ever evolving frontier in data mining
H. Liu, H. Motoda, R. Setiono, and Z. Zhao · 2010
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Running experiments on amazon mechanical turk
G. Paolacci, J. Chandler, and P. G. Ipeirotis · 2010
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L2/l1-optimization in block-sparse compressed sensing and its strong thresholds
M. Stojnic · 2010
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A proximal alternating direction method for l2/l1 norm least squares problem in multi-task feature learning
Y. Xiao, S.-Y. Wu, and B.-S. He · 2012
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Infogather: entity augmentation and attribute discovery by holistic matching with web tables
M. Yakout, K. Ganjam, K. Chakrabarti, and S. Chaudhuri · 2012
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Low rank approximation and regression in input sparsity time
K. L. Clarkson and D. P. Woodruff · 2013
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Data publishing and sharing using fusion tables
A. Y. Halevy · 2013
Goods: Organizing google’s datasets
A. Halevy, F. Korn, N. F. Noy, C. Olston, N. Polyzotis, S. Roy, and S. E. Whang · 2016
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Managing google’s data lake: an overview of the goods system
A. Y. Halevy, F. Korn, N. F. Noy, C. Olston, N. Polyzotis, S. Roy, and S. E. Whang · 2016
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To join or not to join?: Thinking twice about joins before feature selection
A. Kumar, J. Naughton, J. M. Patel, and X. Zhu · 2016
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Robust sparse hyperspectral unmixing with l2/l1 norm
Y. Ma, C. Li, X. Mei, C. Liu, and J. Ma · 2016
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J. M. Phillips · 2016
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Cited alongside, same era.
Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
J. Nelson and H. L. Nguyên · 2013
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Robust unsupervised feature selection
M. Qian and C. Zhai · 2013
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Cited alongside, same era.
Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2013
Cited alongside, same era.
Datahub: Collaborative data science & dataset version management at scale
A. Bhardwaj, S. Bhattacherjee, A. Chavan, A. Deshpande, A. J. Elmore, S. Madden, and A. G. Parameswaran · 2014
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A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 2017
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Neural optimizer search with reinforcement learning
I. Bello, B. Zoph, V. Vasudevan, and Q. V. Le · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
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A demo of the data civilizer system
R. Castro Fernandez, D. Deng, E. Mansour, A. A. Qahtan, W. Tao, Z. Abedjan, A. Elmagarmid, I. F. Ilyas, S. Madden, M. Ouzzani, et al · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Are key-foreign key joins safe to avoid when learning high-capacity classifiers?
V. Shah, A. Kumar, and X. Zhu · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and J. Z. Kolter · 2017
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Table2vec: Neural word and entity embeddings for table population and retrieval
L. Deng · 2018
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Aurum: A data discovery system
R. C. Fernandez, Z. Abedjan, F. Koko, G. Yuan, S. Madden, and M. Stonebraker · 2018
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Probabilistic matrix factorization for automated machine learning
N. Fusi, R. Sheth, and M. Elibol · 2018
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Northstar: An Interactive Data Science System
T. Kraska · 2018
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Dimension-reduced direction-of-arrival estimation based on l2/l1-norm penalty
B. Liu, G. Gui, S. Matsushita, and L. Xu · 2018
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A review of sparse recovery algorithms
E. C. Marques, N. Maciel, L. Naviner, H. Cai, and J. Yang · 2018
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Google dataset search: Building a search engine for datasets in an open web ecosystem
D. Brickley, M. Burgess, and N. Noy · 2019
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Automl: A survey of the state-of-the-art
X. He, K. Zhao, and X. Chu · 2019
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Democratizing data science through interactive curation of ml pipelines
Z. Shang, E. Zgraggen, B. Buratti, F. Kossmann, P. Eichmann, Y. Chung, C. Binnig, E. Upfal, and T. Kraska · 2019
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X. Sun and B. Bischl · 2019
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