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% This file was created with JabRef 2.10b2.
% Encoding: UTF8
@String { aoas = {Ann. Appl. Stat.} }
@String { aos = {Ann. Statist.} }
@String { icml = {ICML} }
@String { jasa = {J. Amer. Statist. Assoc.} }
@String { jmlr = {J. Mach. Learn. Res.} }
@String { nips = {NIPS} }
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Year = {2012},
Month = mar,
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Numpages = {72},
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}
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Title = {\hl{Text referred to this reference is not complete}. Fix it. This is a dummy reference.},
Author = {Wittawat \hl{FixMe}},
Journal = {Journal of Fixing Thesis Bugs.},
Year = {2017},
Owner = {wittawat},
Timestamp = {2017.07.13}
}
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Title = {On the concept of approximate sufficiency},
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Date-added = {2015-01-30 14:04:15 +0000},
Date-modified = {2015-01-30 14:05:46 +0000}
}
@Article{Aeschbacher12,
Title = {{A Novel Approach for Choosing Summary Statistics in Approximate {Bayesian} Computation}},
Author = {Aeschbacher, S. and Beaumont, M. A. and Futschik, A.},
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Date-modified = {2015-02-03 14:15:37 +0000}
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Title = {Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates},
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Owner = {wittawat},
Timestamp = {2017.07.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0047259X84710335}
}
@Book{Anderson2003,
Title = {An {Introduction} to {Multivariate} {Statistical} {Analysis}},
Author = {Theodore Wilbur Anderson},
Publisher = {Wiley},
Year = {2003},
Abstract = {Perfected over three editions and more than forty years, this field- and classroom-tested reference:* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.* Treats all the basic and important topics in multivariate statistics.* Adds two new chapters, along with a number of new sections.* Provides the most methodical, up-to-date information on MV statistics available.},
Keywords = {Mathematics / Probability \& Statistics / Multivariate Analysis, Mathematics / Probability \& Statistics / Stochastic Processes},
Language = {en}
}
@Article{Arc1992,
Title = {Large deviations for U-statistics},
Author = {Arcones, Miguel A},
Year = {1992},
Number = {2},
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Doi = {10.1016/0047-259X(92)90049-L},
ISSN = {0047-259X},
Journaltitle = {Journal of Multivariate Analysis},
Keywords = {-statistics, large deviations},
Owner = {nuke},
Shortjournal = {Journal of Multivariate Analysis},
Timestamp = {2017.03.28},
Url = {http://www.sciencedirect.com/science/article/pii/0047259X9290049L},
Urldate = {2017-03-21}
}
@Article{ArcGin1992,
Title = {On the bootstrap of {U} and {V} statistics},
Author = {Arcones, Miguel A and Gine, Evarist},
Journal = {The Annals of Statistics},
Year = {1992},
Pages = {655--674},
Owner = {wittawat},
Publisher = {JSTOR},
Timestamp = {2017.07.19}
}
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Title = {Towards Principled Methods for Training Generative Adversarial Networks},
Author = {{Arjovsky}, M. and {Bottou}, L.},
Journal = {ArXiv e-prints},
Year = {2017},
Month = jan,
Archiveprefix = {arXiv},
Eprint = {1701.04862},
Keywords = {Statistics - Machine Learning, Computer Science - Learning},
Primaryclass = {stat.ML}
}
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Title = {Kernel Independent Component Analysis},
Author = {Bach, Francis R. and Jordan, Michael I.},
Journal = {Journal of Machine Learning Research},
Year = {2002},
Pages = {1--48},
Volume = {3},
Owner = {wittawat},
Timestamp = {2017.06.23}
}
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Title = {Rates of Convergence of Estimates and Test Statistics},
Author = {Bahadur, R. R.},
Year = {1967},
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Volume = {38},
Abstract = {Project Euclid - mathematics and statistics online},
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ISSN = {0003-4851, 2168-8990},
Journaltitle = {The Annals of Mathematical Statistics},
Mrnumber = {MR207085},
Owner = {nuke},
Shortjournal = {Ann. Math. Statist.},
Timestamp = {2017.03.28},
Url = {http://projecteuclid.org/euclid.aoms/1177698949},
Urldate = {2017-03-22},
Zmnumber = {0201.52106}
}
@Article{Bah1960,
Title = {Stochastic Comparison of Tests},
Author = {Bahadur, R. R.},
Journal = {The Annals of Mathematical Statistics},
Year = {1960},
Number = {2},
Pages = {276--295},
Volume = {31},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
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Title = {Effect of high dimension: by an example of a two sample problem},
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Pages = {311--329},
Publisher = {JSTOR}
}
@Article{Barankin1963,
Title = {{Generalization of the Fisher-Darmois-Koopman-Pitman Theorem on Sufficient Statistics}},
Author = {Barankin, E.W. and Maitra, A.P.},
Journal = {Sankhya: The Indian Journal of Statistics, Series A},
Year = {1963},
Number = {3},
Pages = {pp. 217-244},
Volume = {25}
}
@Article{Barber2013,
Title = {The Rate of Convergence for Approximate {Bayesian} Computation},
Author = {{Barber}, S. and {Voss}, J. and {Webster}, M.},
Journal = {ArXiv e-prints:1311.2038},
Year = {2013},
Archiveprefix = {arXiv},
Eprint = {1311.2038},
Keywords = {Mathematics - Statistics Theory, 62F12, 62F15, 65C05},
Primaryclass = {math.ST}
}
@Article{Baringhaus2004,
Title = {On a new multivariate two-sample test},
Author = {Ludwig Baringhaus and Carsten Franz},
Journal = {Journal of Multivariate Analysis},
Year = {2004},
Pages = {190-206},
Volume = {88}
}
@Article{BaringhausHenze88,
Title = {A consistent test for multivariate normality based on the empirical characteristic function},
Author = {L. Baringhaus and N. Henze},
Journal = {Metrika},
Year = {1988},
Pages = {339--348},
Volume = {35},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
@InProceedings{Barthelme2011,
Title = {{ABC-EP}: Expectation Propagation for Likelihood-free {B}ayesian Computation },
Author = {Simon Barthelm\'{e} and Nicolas Chopin},
Booktitle = {ICML},
Year = {2011},
Pages = {289--296}
}
@Article{Bazinetal_2010,
Title = {Likelihood-Free Inference of Population Structure and Local Adaptation in a {Bayesian} Hierarchical Model},
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Journal = {Genetics},
Year = {2010},
Month = {06},
Number = {2},
Pages = {587--602},
Volume = {185},
Bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881139/},
Bdsk-url-2 = {http://dx.doi.org/10.1534/genetics.109.112391},
Date = {2010/06/},
Date-added = {2015-01-30 13:46:55 +0000},
Date-modified = {2015-02-03 14:16:22 +0000},
J1 = {Genetics}
}
@Article{Beaumont02,
Title = {Approximate {Bayesian} Computation in Population Genetics},
Author = {Beaumont, M. A. and Zhang, W. and Balding, D. J.},
Journal = {Genetics},
Year = {2002},
Number = {4},
Pages = {2025-2035},
Volume = {162},
Date-modified = {2015-02-03 14:16:36 +0000}
}
@Article{BeiGyoLug94,
Title = {On the asymptotic normality of the $L_1$- and $L_2$-errors in histogram density estimation},
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Owner = {wittawat},
Timestamp = {2017.06.27}
}
@Book{BerTho04,
Title = {Reproducing Kernel Hilbert Spaces in Probability and Statistics},
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Publisher = {Kluwer},
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Owner = {wittawat},
Timestamp = {2017.06.23}
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@Book{Bha2013,
Title = {Matrix analysis},
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Publisher = {Springer Science \& Business Media},
Year = {2013},
Volume = {169},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
@Book{Bilodeau2008,
Title = {Theory of multivariate statistics},
Author = {Bilodeau, Martin and Brenner, David},
Publisher = {Springer Science \& Business Media},
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@Book{Bird2009,
Title = {Natural {Language} {Processing} with {Python}},
Author = {Bird, Steven and Klein, Ewan and Loper, Edward},
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@Book{Bishop2006,
Title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
Author = {Bishop, Christopher M.},
Publisher = {Springer-Verlag New York, Inc.},
Year = {2006},
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Owner = {wittawat},
Timestamp = {2014.10.07}
}
@Article{blum2013,
Title = {{A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation}},
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Title = {A Test of Relative Similarity For Model Selection in Generative Models},
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Journal = {ArXiv e-prints},
Year = {2015},
Month = nov,
Eprint = {1511.04581},
Keywords = {Statistics - Machine Learning, Computer Science - Learning},
Primaryclass = {stat.ML}
}
@Article{Bousquet2003,
Title = {New approaches to statistical learning theory},
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Journal = {Annals of the Institute of Statistical Mathematics},
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Volume = {55}
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@Article{BowFos93,
Title = {Adaptive smoothing and density based tests of multivariate normality},
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Volume = {88},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
@Article{Breiman2001,
Title = {Random {Forests}},
Author = {Breiman, Leo},
Journal = {Machine Learning},
Year = {2001},
Month = oct,
Number = {1},
Pages = {5--32},
Volume = {45},
Keywords = {classification, ensemble, regression},
Owner = {wittawat},
Timestamp = {2015.02.23},
Urldate = {2015-02-23}
}
@Article{cam1964,
Title = {Sufficiency and Approximate Sufficiency},
Author = {Cam, L. Le.},
Journal = {Ann. Math. Statist.},
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Number = {4},
Pages = {1419--1455},
Volume = {35},
Bdsk-url-1 = {http://dx.doi.org/10.1214/aoms/1177700372},
Date-added = {2015-01-30 14:00:50 +0000},
Date-modified = {2015-02-04 15:58:40 +0000},
Fjournal = {The Annals of Mathematical Statistics}
}
@Article{Caponnetto2007,
Title = {Optimal Rates for the Regularized Least-Squares Algorithm},
Author = {Caponnetto, A. and De Vito, E.},
Journal = {Found. Comput. Math.},
Year = {2007},
Month = jul,
Number = {3},
Pages = {331--368},
Volume = {7},
Acmid = {1290534},
Address = {Secaucus, NJ, USA},
Doi = {10.1007/s10208-006-0196-8},
ISSN = {1615-3375},
Issue_date = {July 2007},
Numpages = {38},
Owner = {wittawat},
Publisher = {Springer-Verlag New York, Inc.},
Timestamp = {2014.10.09},
Url = {http://dx.doi.org/10.1007/s10208-006-0196-8}
}
@Article{Carmeli2010,
Title = {Vector valued reproducing kernel {H}ilbert spaces and universality},
Author = {Carmeli, C. and De Vito, E. and Toigo, A. and Umanit\`{a}, V.},
Journal = {Analysis and Applications},
Year = {2010},
Month = jan,
Number = {01},
Pages = {19--61},
Volume = {08},
Owner = {wittawat},
Timestamp = {2017.06.27},
Urldate = {2017-01-05}
}
@Article{CheLiJacBenLi2016,
Title = {Mode regularized generative adversarial networks},
Author = {Che, Tong and Li, Yanran and Jacob, Athul Paul and Bengio, Yoshua and Li, Wenjie},
Journal = {arXiv preprint arXiv:1612.02136},
Year = {2016}
}
@Article{CheWelSmo2012,
Title = {Super-Samples from Kernel Herding},
Author = {Chen, Yutian and Welling, Max and Smola, Alex},
Year = {2012},
Eprint = {1203.3472},
Eprinttype = {arxiv},
File = {arXiv\:1203.3472 PDF:/home/nuke/.mozilla/firefox/n4nyekc3.default/zotero/storage/7W35R4JX/Chen et al. - 2012 - Super-Samples from Kernel Herding.pdf:application/pdf;arXiv.org Snapshot:/home/nuke/.mozilla/firefox/n4nyekc3.default/zotero/storage/CEUKFPRK/1203.html:text/html},
Journaltitle = {{arXiv}:1203.3472 [cs, stat]},
Keywords = {Computer Science - Learning, Statistics - Machine Learning},
Owner = {wittawat},
Timestamp = {2017.06.27},
Url = {http://arxiv.org/abs/1203.3472},
Urldate = {2014-07-16}
}
@Article{Che1952,
Title = {A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations},
Author = {Chernoff, Herman},
Year = {1952},
Number = {4},
Pages = {493--507},
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Timestamp = {2017.06.27}
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Timestamp = {2017.06.27}
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Owner = {wittawat},
Timestamp = {Wed, 10 Dec 2014 21:34:12 +0100}
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Author = {{DasGupta}, Anirban},
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Keywords = {Mathematics / Probability \& Statistics / General, Mathematics / Probability \& Statistics / Stochastic Processes},
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Pagetotal = {726},
Timestamp = {2017.03.28}
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Language = {English},
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Date-added = {2015-06-03 09:07:27 +0000},
Date-modified = {2015-06-03 09:07:31 +0000},
Doi = {10.1214/14-STS498},
Fjournal = {Statistical Science},
Publisher = {The Institute of Mathematical Statistics},
Url = {http://dx.doi.org/10.1214/14-STS498}
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Year = {2013}
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Pages = {258-267}
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Timestamp = {2017.06.27}
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Title = {{Just-In-Time Learning for Fast and Flexible Inference}},
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Year = {2014},
Pages = {154--162}
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Owner = {wittawat},
Timestamp = {2017.06.27}
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Title = {Kernels based tests with non-asymptotic bootstrap approaches for two-sample problems},
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Pages = {1871--1905},
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Timestamp = {2017.06.23}
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Title = {Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces},
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Title = {Kernel Measures of Conditional Dependence},
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Year = {2008},
Pages = {489--496},
Owner = {wittawat},
Timestamp = {2017.06.23}
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Journal = jmlr,
Year = {2013},
Pages = {3753-3783},
Volume = {14},
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@Misc{Fukumizu2010,
Title = {Kernel Bayes' rule},
Author = {Fukumizu, Kenji and Song, Le and Gretton, Arthur},
Year = {2010},
Abstract = {A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RKHS functions of an empirical sample: no explicit parametric model is needed for these quantities. The posterior is likewise an RKHS mean of a weighted sample. The estimator for the expectation of a function of the posterior is derived, and rates of consistency are shown. Some representative applications of the kernel Bayes' rule are presented, including Baysian computation without likelihood and filtering with a nonparametric state-space model.},
Added-at = {2014-04-16T18:50:14.000+0200},
Biburl = {http://www.bibsonomy.org/bibtex/2feb6401b9964b4cac3ee522b2878e4b5/wittawatj},
Description = {[1009.5736] Kernel Bayes' rule},
Interhash = {ec7f63bc48617e77473598da871cbb39},
Intrahash = {feb6401b9964b4cac3ee522b2878e4b5},
Keywords = {embedding kernel},
Timestamp = {2014-04-16T18:50:14.000+0200},
Url = {http://arxiv.org/abs/1009.5736}
}
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Number = {1},
Pages = {49--58},
Volume = {5},
Address = {New York, NY, USA},
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Pages = {1--19},
Volume = {1},
Abstract = {Abstract. Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral-t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of ânoninformative â prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the half-t family when the number of groups is small and in other settings where a weakly informative prior is desired. We also illustrate the use of the half-t family for hierarchical modeling of multiple variance parameters such as arise in the analysis of variance.},
File = {Citeseer - Full Text PDF:/nfs/nhome/live/wittawat/.zotero/zotero/q6a3aco7.default/zotero/storage/576Z94H4/Gelman - 2006 - Prior distributions for variance parameters in hie.pdf:application/pdf},
Owner = {wittawat},
Timestamp = {2015.02.17}
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Pages = {3--42},
Volume = {63},
Urldate = {2015-02-23}
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Title = {Approximate {Bayesian} computation with indirect summary statistics},
Author = {Gleim, A. and Pigorsch, C.},
Journal = {Draft paper: http://ect-pigorsch. mee. uni-bonn. de/data/research/papers},
Year = {2013},
Date-added = {2015-06-03 10:14:50 +0000},
Date-modified = {2015-06-03 10:14:56 +0000}
}
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Owner = {wittawat},
Timestamp = {2017.06.27},
Urldate = {2017-03-22}
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Owner = {nuke},
Timestamp = {2017.06.27},
Urldate = {2017-03-21}
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Title = {Generative Adversarial Nets},
Author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
Booktitle = {NIPS},
Year = {2014},
Pages = {2672--2680},
Url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf}
}
@InProceedings{GooManRoyBonTen08,
Title = {Church: A language for generative models},
Author = {N.D. Goodman and V.K. Mansinghka and D.M. Roy and K. Bonawitz and J.B. Tenenbaum},
Booktitle = {UAI},
Year = {2008},
Pages = {220--229}
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@Article{GorMac2017,
Title = {Measuring Sample Quality with Kernels},
Author = {Gorham, Jackson and Mackey, Lester},
Year = {2017},
Note = {arXiv: 1703.01717},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
@InProceedings{GorMac2015,
Title = {Measuring Sample Quality with {S}tein's Method},
Author = {Gorham, Jackson and Mackey, Lester},
Booktitle = {NIPS},
Year = {2015},
Pages = {226--234},
Owner = {wittawat},
Timestamp = {2017.06.27}
}
@InProceedings{Grunewalder2012,
Title = {Conditional mean embeddings as regressors},
Author = {Gr{\"{u}}new{\"{a}}lder, S. and Lever, G. and Gretton, A. and Baldassarre, L. and Patterson, S. and Pontil, M.},
Booktitle = {ICML},
Year = {2012},
Bibsource = {dblp computer science bibliography, http://dblp.org},
Biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/GrunewalderLGBPP12},
Date-modified = {2015-02-03 14:18:00 +0000},
Timestamp = {Fri, 25 Jan 2013 09:27:52 +0100}
}
@InProceedings{Gruenewaelder2012,
Title = {Conditional mean embeddings as regressors},
Author = {Gr{\"u}new{\"a}lder, S. and Lever, G. and Baldassarre, L. and Patterson, S. and Gretton, A. and Pontil, M.},
Booktitle = {Proceedings of the 29th International Conference on Machine Learning},
Year = {2012},
Address = {New York, NY, USA},
Editor = {Langford, J and Pineau, J},
Pages = {1823--1830},
Publisher = {Omnipress},
Department = {Department Sch{\"o}lkopf},
Event_name = {ICML 2012},
Event_place = {Edinburgh, Scotland, GB},
Url = {http://icml.cc/2012/papers/898.pdf},
Web_url = {http://icml.cc/2012/papers/898.pdf}
}
@TechReport{Gretton15,
Title = {A simpler condition for consistency of a kernel independence test},
Author = {Arthur Gretton},
Year = {2015},
Owner = {wittawat},
Timestamp = {2017.06.23},
Url = {http://arxiv.org/abs/1501.06103}
}
@Article{Gretton2012,
Title = {A Kernel Two-Sample Test},
Author = {Gretton, A. and Borgwardt, K. and Rasch, M. and Sch\"{o}lkopf, B. and Smola, A.},