@article{glmnet,
    author    = {Jerome Friedman and
                 Trevor Hastie and
                 Rob Tibshirani},
    title     = {\textbf{Regularization Paths for Generalized Linear Models via Coordinate Descent}},
    journal   = {\textit{Journal of Statistical Software}},
    volume    = {33},
    number    = {1},
    pages     = {},
    year      = {2009},
    url       = {http://core.ac.uk/download/pdf/6287975.pdf}
}

@article{strongrules,
    author    = {Jacob Bien and
		 Jerome Friedman and
                 Trevor Hastie and
		 Noah Simon and 
		 Jonathan Taylor and 
                 Rob Tibshirani and 
		 Ryan J. Tibshirani},
    title     = {\textbf{Strong Rules for Discarding Predictors in Lasso-type Problems}},
    journal   = {\textit{Journal of the Royal Statistical Society. Series B}},
    volume    = {74},
    number    = {1},
    pages     = {},
    year      = {2012},
    url       = {http://statweb.stanford.edu/~tibs/ftp/strong.pdf}
}

@ARTICLE{elastic,
    author = {Hui Zou and Trevor Hastie},
    title = {\textbf{Regularization and variable selection via the Elastic Net}},
    journal = {\textit{Journal of the Royal Statistical Society. Series B}},
    year = {2005},
    volume = {67},
    pages = {301--320}
}

@article{lasso,
    author    = {Rob Tibshirani},
    title     = {\textbf{Regression Shrinkage and Selection via the Lasso}},
    journal   = {\textit{Journal of the Royal Statistical Society. Series B (Methodological)}},
    volume    = {58},
    number    = {1},
    pages     = {267-288},
    year      = {1996},
    url       = {http://statweb.stanford.edu/~tibs/lasso/lasso.pdf}
}

@article{admm,
    author    = {Stephen Boyd and 
		 Neal Parikh and 
		 Eric Chu and 
		 Borja Peleato and 
		 Jonahtan Eckstein},
    title     = {\textbf{Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers}},
    journal   = {\textit{Foundations and Trends in Machine Learning}},
    volume    = {3},
    number    = {1},
    pages     = {1-122},
    year      = {1996},
    url       = {https://web.stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf}
}

@misc{cliffgbm,
    author    = {Cliff Click},
    title     = {\textbf{Building a Distributed GBM on H2O}},
    journal   = {{H2O.ai Blog}},
    year      = {2013},
    url       = {{http://h2o.ai/blog/2013/10/building-distributed-gbm-h2o/}}
}

@article{bias,
    author    = {Thomas Dietterich and 
		 Eun Bae Kong},
    title     = {\textbf{Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms}},
    year      = {1995},
    url       = {http://www.iiia.csic.es/~vtorra/tr-bias.pdf},
}

@article{boost,
    author    = {Jane Elith and
                 John R. Leathwick and
                 Trevor Hastie},
    title     = {\textbf{A Working Guide to Boosted Regression Trees}},
    journal   = {\textit{Journal of Animal Ecology}},
    volume    = {77},
    number    = {4},
    pages     = {802-813},
    year      = {2008},
    url       = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2008.01390.x/abstract}
}

@article{greedyfunction,
    author = {Jerome H. Friedman},
    title = {\textbf{Greedy Function Approximation: A Gradient Boosting Machine}},
    journal = {\textit{Annals of Statistics}},
    year = {1999},
    volume = {29},
    pages = {1189--1232},
    url   = {http://statweb.stanford.edu/?jhf/ftp/trebst.pdf}
}

@article{prox,
    author    = {Neal Parikh and
                 Stephen Boyd},
    title     = {\textbf{Proximal Algorithms}},
    journal   = {\textit{Foundations and Trends in Optimization}},
    volume    = {1},
    number    = {3},
    pages     = {123-231},
    year      = {2014},
    url       = {http://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf}
}

@article{discussion,
    author    = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title     = {\textbf{Discussion of Boosting Papers}},
    journal   = {\textit{Annual Statistics}},
    volume    = {32},
    pages     = {102-107},
    year      = {2004},
    url       = {http://web.stanford.edu/?hastie/Papers/boost_discussion.pdf}
}

@article{additivelogistic,
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title = {\textbf{Additive Logistic Regression: a Statistical View of Boosting}},
    journal = {\textit{Annals of Statistics}},
    year = {1998},
    volume = {28},
    pages = {2000},
    url = {http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle= euclid.aos/1016218223} 
}

@book{esl,
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    title = {\textbf{The Elements of Statistical Learning}},
    booktitle = {\textit{The Elements of Statistical Learning}},
    publisher = {Springer},
    address = {New York},
    year = {2001},
    url = {http://statweb.stanford.edu/?tibs/ElemStatLearn/printings/ESLII_print10.pdf}
}

@misc{performance,
    author = {Candel, Arno},
    title = {\textbf{Definitive Performance Tuning Guide for Deep Learning}},
    journal = {H2O.ai Blog},
    type = {Blog},
    number = {February 26},
    year = {2015},
    howpublished = {\url{http://h2o.ai/blog/2015/02/deep-learning-performance/}}
}


@article{neuralnet,
    author = {G.E. Hinton and R.R. Salakhutdinov},
    title = {\textbf{Reducing the Dimensionality of Data with Neural Networks}},
    journal = {\textit{Science}},
    volume = {313},
    year = {2006},
    pages = {504--507},
    url = {http://www.cs.toronto.edu/~hinton/science.pdf}
}

@online{mnist,
	author = {LeCun, Yann and Cortes, Corinna and Burgres, Christopher J.C},
	title = {\textbf{The MNIST Database}},
	url = {http://yann.lecun.com/exdb/mnist/}
}

@online{h2ogithubdl,
        author = {H2O.ai Team},
        title = {\textbf{H2O GitHub repository for Deep Learning documentation}},
        url = {https://github.com/h2oai/h2o/tree/master/docs/deeplearning/DeepLearningRVignetteDemo}, 
	year = {\the\year}
}

@misc{h2osite,
        author = {H2O.ai Team},
        title = {\textbf{H2O website}},
        journal = {H2O.ai},
	url = {http://h2o.ai},
	year = {\the\year}
}

@misc{h2odocs,
        author = {H2O.ai Team},
        title = {\textbf{H2O documentation}},        
        url = {http://docs.h2o.ai},
	year = {\the\year}
}

@misc{h2ogithubrepo,
        author = {H2O.ai Team},
        title = {\textbf{H2O GitHub Repository}},
        url = {https://github.com/h2oai},
	year = {\the\year}
}

@misc{h2odatasets,
        author = {H2O.ai Team},
        title = {\textbf{H2O Datasets}},
        url = {http://data.h2o.ai},
	year = {\the\year}
}


@misc{h2ojira,
        author = {H2O.ai Team},
        title = {\textbf{H2O JIRA}},
        url = {https://jira.h2o.ai},
	year = {\the\year}
}

@misc{dlarchitecture,
    author = {Yoshua Bengio},
    title = {\textbf{Learning Deep Architectures for AI}},
    year = {2009},
    url = {http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf}
}

@misc{efficientbackprop,
    author = {Yann Lecun and Leon Bottou and Genevieve B. Orr and Klaus-Robert M\"uller},
    title = {\textbf{Efficient BackProp}},
    year = {1998}, 
    url = {http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf}
}

@INPROCEEDINGS{maxoutnetworks,
    author = {Ian J. Goodfellow and David Warde-farley and Mehdi Mirza and Aaron Courville and Yoshua Bengio},
    title = {\textbf{Maxout networks}},
    booktitle = {\textit{In ICML}},
    year = {2013},
    url = {http://arxiv.org/pdf/1302.4389.pdf}
}


@INPROCEEDINGS{hogwild,
    author = {Feng Niu and Benjamin Recht and Christopher R\'e and Stephen J. Wright},
    title = {\textbf{Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent}},
    booktitle = {In NIPS},
    year = {2011}
}

@inproceedings{adadelta,
    author = {Matthew D. Zeiler},
    title = {\textbf{ADADELTA: An Adaptive Learning Rate Method}},
    year = {2012},
    url = {http://arxiv.org/pdf/1212.5701v1.pdf}
}

@article{momentum,
    author = {Ilya Sutskever and James Martens and George Dahl and Geoffrey Hinton},
    title = {\textbf{On the importance of initialization and momentum in deep learning}},
    year = {2014},
    url = {http://www.cs.toronto.edu/~fritz/absps/momentum.pdf}
}

@article{featuredetectors,
    author = {G. E. Hinton and N. Srivastava and A. Krizhevsky and I. Sutskever and R. R. Salakhutdinov},
    title = {\textbf{Improving neural networks by preventing co-adaptation of feature detectors}},
    year = {2012},
    url = {http://arxiv.org/pdf/1207.0580.pdf}
}

@online{rdocs,
        author = {H2O.ai Team},
        title = {\textbf{H2O R Package Documentation}},
        url = {http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Rdoc.html}, 
	year = {\the\year}
}

@Manual{h2o_GLM_booklet,
    title = {\textbf{Generalized Linear Modeling with H2O}},
    author = {Hussami, N. and Kraljevic, T. and Lanford, J. and Nykodym, T. and Rao, A. and Wang, A.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_DL_booklet,
    title = {\textbf{Deep Learning with H2O}},
    author = {Arora, A. and Candel, A. and Lanford, J. and LeDell, E. and and Parmar, V.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_GBM_booklet,
    title = {\textbf{Gradient Boosted Models}},
    author = {Click, C. and Lanford, J. and Malohlava, M. and Parmar, V. and Roark, H.},
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_R_booklet,
    title = {\textbf{Fast Scalable R with H2O}},
    author = {Aiello, S. and Eckstrand, E. and Fu, A. and Landry, M. and Lanford, J. and Aboyoun, P. },
    year = {2015},
    month = {August},
    url = {http://h2o.ai/resources},
}

@Manual{h2o_R_package,
    title = {\textbf{h2o: R Interface for H2O}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {R package version 3.1.0.99999},
    url = {http://www.h2o.ai},
}


@Manual{h2o_Python_module,
    title = {\textbf{h2o: Python Interface for H2O}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {Python package version 3.1.0.99999},
    url = {http://www.h2o.ai},
}


@Manual{h2o_Java_software,
    title = {\textbf{H2O: Scalable Machine Learning}},
    author = {H2O.ai Team},
    year = {\the\year},
    note = {version 3.1.0.99999},
    url = {http://www.h2o.ai},
}


@misc{stream,
        author = {H2O.ai Team},
        title = {\textbf{H2Ostream}},
        url = {https://groups.google.com/d/forum/h2ostream},
	year = {\the\year}
}

@misc{rensemble, 
	author = {Erin LeDell}, 
	title = {\textbf{R Ensemble Documentation}}, 
	url = {http://www.stat.berkeley.edu/~ledell/R/h2oEnsemble.pdf},
	year = {2014} 
}

@Manual{r,
  title        = {R: A Language and Environment for Statistical
                  Computing},
  author       = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address      = {Vienna, Austria},
  year         = 2015,
  url          = {https://www.R-project.org}
}

@misc{Pydocs,
	author = {H2O.ai Team},
	title = {\textbf{H2O Python Documentation}}, 
	url = {http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Pydoc.html},
	year = {2015}
}

@inproceedings{lenet,
    author = {Yann LeCun and Bottou, Leon and Bengio, Yoshua and Haffner, Patrick},
    title = {\textbf{Gradient-Based Learning Applied to Document Recognition}},
    booktitle = {\textit{Proceedings of the IEEE}},
    year = {Nov. 1998},
    url = {http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf}
}

@incollection{alexnet,
	author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
	title = {\textbf{ImageNet Classification with Deep Convolutional Neural Networks}},
	booktitle = {Advances in Neural Information Processing Systems 25},
	editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
	pages = {1097--1105},
	year = {2012},
	url = {https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
}

@article{vgg,
    author = {Simonyan, Karen and Zisserman, Andrew},
    title = {\textbf{Very Deep Convolutional Networks for Large-Scale Image Recognition}},
    year = {2014},
    url = {http://arxiv.org/pdf/1207.0580.pdf}
}

@article{googlenet,
    author = {Christian Szegedy and
               Wei Liu and
               Yangqing Jia and
               Pierre Sermanet and
               Scott E. Reed and
               Dragomir Anguelov and
               Dumitru Erhan and
               Vincent Vanhoucke and
               Andrew Rabinovich},
    title = {\textbf{Going Deeper with Convolutions}},
    year = {2014},
    url = {https://arxiv.org/pdf/1409.4842.pdf}
}

@article{inceptionbn,
    author = {Sergey Ioffe and Christian Szegedy},
    title = {\textbf{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
    year = {2015},
    url = {https://arxiv.org/pdf/1502.03167.pdf}
}

@article{resnet,
    author = {Kaiming He and
							 Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
    title = {\textbf{Deep Residual Learning for Image Recognition}},
    year = {2015},
    url = {https://arxiv.org/pdf/1512.03385.pdf}
}

@article{bergstra-bengio,
 author = {Bergstra, James and Bengio, Yoshua},
 title = {\textbf{Random Search for Hyper-parameter Optimization}},
 journal = {J. Mach. Learn. Res.},
 issue_date = {3/1/2012},
 volume = {13},
 month = Feb,
 year = {2012},
 issn = {1532-4435},
 pages = {281--305},
 numpages = {25},
 url = {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf},
 publisher = {JMLR.org}
}

