text:
title = {Computing Multi-Relational Sufficient Statistics for Large Databases},
  booktitle = {Computational Intelligence and Knowledge Management (CIKM)},
  year = {2014},
  pages = {1249--1258},
  abstract = {Acceptance Rate 20\%.},
  ee = {http://dx.doi.org/10.1145/2661829.2662010},
  owner = {zqian},
  timestamp = {2014.08.22}
}

@MASTERSTHESIS{Quakkelaar2017,
  author = {Quakkelaar, Rene},
  title = {Exploiting relational database technology for statistical machine
	learning in FactorBase},
  school = {Open Universiteit Nederland},
  year = {2017},
  type = {Master Thesis},
  month = {jul}
}

@ARTICLE{bib:quine,
  author = {Willard Quine},
  title = {Two Dogmas of Empiricism},
  journal = {Philosophical Review},
  year = {1951},
  volume = {60},
  pages = {20--43},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INCOLLECTION{bib:quine-ont,
  author = {W. V. Quine},
  title = {Natural Kinds},
  booktitle = {Ontological Relativity and Other Essays},
  publisher = {Columbia University Press},
  year = {1969},
  pages = {114-138},
  address = {New York and London},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Quinlan1996,
  author = {Quinlan, J.},
  title = {Boosting first-order learning},
  booktitle = {Algorithmic Learning Theory},
  year = {1996},
  pages = {143--155},
  organization = {Springer},
  owner = {aaa78},
  timestamp = {2011.10.05}
}

@BOOK{c4.5,
  title = {C4.5: programs for machine learning},
  publisher = {Morgan Kaufmann Publishers Inc.},
  year = {1993},
  author = {Quinlan, J. Ross},
  isbn = {1-55860-238-0}
}

@ARTICLE{Quinlan1990,
  author = {J. Ross Quinlan},
  title = {Learning Logical Definitions from Relations},
  journal = {Machine Learning},
  year = {1990},
  volume = {5},
  pages = {239-266},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  keywords = {statistical-relational}
}

@ARTICLE{Quinlan90,
  author = {J. R. Quinlan},
  title = {Learning Logical Definitions from Relations},
  journal = {Machine Learning},
  year = {1990},
  volume = {5},
  pages = {239--266},
  owner = {oliverschulte},
  publisher = {Kluwer Academic Publishers, Boston},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{foil,
  author = {J. Ross Quinlan and R. Mike Cameron-Jones},
  title = {FOIL: A Midterm Report.},
  booktitle = {ECML},
  year = {1993},
  volume = {667},
  pages = {3-20},
  publisher = {Springer}
}

@ARTICLE{Quinlan1989,
  author = {J. Ross Quinlan and Ronald L. Rivest},
  title = {Inferring Decision Trees Using the Minimum Description Length Principle},
  journal = {Inf. Comput.},
  year = {1989},
  volume = {80},
  pages = {227-248},
  number = {3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://dx.doi.org/10.1016/0890-5401(89)90010-2}
}

@INPROCEEDINGS{Quinlan1992,
  author = {Ross J. Quinlan},
  title = {Learning with Continuous Classes},
  booktitle = {5th Australian Joint Conference on Artificial Intelligence},
  year = {1992},
  pages = {343-348},
  address = {Singapore},
  publisher = {World Scientific}
}

@INPROCEEDINGS{Rabiner1989,
  author = {Rabiner, Lawrence R.},
  title = {A tutorial on hidden markov models and selected applications in speech
	recognition},
  booktitle = {Proceedings of the IEEE},
  year = {1989},
  volume = {77},
  pages = {257--286},
  abstract = {Although initially introduced and studied in the late 1960s and early
	1970s, statistical methods of Markov source or hidden Markov modeling
	have become increasingly popular in the last several years. There
	are two strong reasons why this has occurred. First the models are
	very rich in mathematical structure and hence can form the theoretical
	basis for use in a wide range of applications. Sec-ond the models,
	when applied properly, work very well in practice for several important
	applications. In this paper we attempt to care-fully and methodically
	review the theoretical aspects of this type of statistical modeling
	and show how they have been applied to selected problems in machine
	recognition of speech. I.},
  citeulike-article-id = {4037759},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.2084},
  keywords = {hmm},
  owner = {hkhosrav},
  posted-at = {2009-02-12 07:24:46},
  priority = {2},
  timestamp = {2009.08.11},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.2084}
}

@INPROCEEDINGS{hybridClassifier,
  author = {Rajat Raina and Yirong Shen and Andrew Y. Ng and Andrew Mccallum},
  title = {Classification with Hybrid Generative/Discriminative Models},
  booktitle = {NIPS},
  year = {2003},
  pages = {545-552},
  publisher = {MIT Press},
  file = {:Z\:\\Subverrsion\\oschulte-texmf\\hybridclassifier.pdf:PDF}
}

@BOOK{Rajaraman2011,
  title = {Mining of Massive Datasets},
  publisher = {Cambridge University Press},
  year = {2011},
  author = {Rajaraman, Anand and Ullman, Jeffrey David},
  address = {New York, NY, USA},
  file = {:Rajaraman2011.pdf:PDF},
  owner = {Kurt},
  timestamp = {2014.08.19}
}

@BOOK{Ramakrishnan2003,
  title = {Database Management Systems},
  publisher = {McGraw-Hill},
  year = {2003},
  author = {Raghu Ramakrishnan and Johannes Gehrke},
  edition = {3rd},
  owner = {oschulte},
  timestamp = {2012.06.08}
}

@ARTICLE{Ramon2008,
  author = {Jan Ramon and Tom Croonenborghs and Daan Fierens and Hendrik Blockeel
	and Maurice Bruynooghe},
  title = {Generalized ordering-search for learning directed probabilistic logical
	models},
  journal = {Machine Learning},
  year = {2008},
  volume = {70},
  pages = {169-188},
  number = {2-3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://dx.doi.org/10.1007/s10994-007-5033-7},
  owner = {oschulte},
  timestamp = {2011.09.21}
}

@INPROCEEDINGS{Ramsey2006,
  author = {J. Ramsey and P. Spirtes and J. Zhang},
  title = {Adjacency-Faithfulness and Conservative Causal Inference},
  booktitle = {UAI},
  year = {2006},
  pages = {401--408},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Ravkic2015,
  author = {Ravkic, Irma and Ramon, Jan and Davis, Jesse},
  title = {Learning relational dependency networks in hybrid domains},
  journal = {Machine Learning},
  year = {2015},
  note = {Accepted},
  file = {:/Users/olivernew/Documents/svn-projects/punch-srl/texmf/Ravkic2015.pdf:PDF},
  keywords = {statistical-relational},
  url = {https://lirias.kuleuven.be/handle/123456789/481217}
}

@BOOK{reichenbach49:_theor_probab,
  title = {The Theory of Probability},
  publisher = {Cambridge University Press},
  year = {1949},
  author = {Reichenbach, H.},
  address = {London},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Reischuk1998,
  author = {R. Reischuk and T. Zeugmann},
  title = {Learning one-variable pattern languages in linear average time},
  booktitle = {Proceedings of COLT 1998},
  year = {1998},
  pages = {198--208},
  publisher = {ACM Press},
  doi = {http://doi.acm.org/10.1145/279943.279984},
  isbn = {1-58113-057-0},
  location = {Madison, Wisconsin, United States},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Reiter1989,
  author = {Reiter, Raymond and Mackworth, Alan K},
  title = {A logical framework for depiction and image interpretation},
  journal = {Artificial Intelligence},
  year = {1989},
  volume = {41},
  pages = {125--155},
  number = {2},
  publisher = {Elsevier}
}

@INPROCEEDINGS{Rendle2011,
  author = {Rendle, Steffen and Gantner, Zeno and Freudenthaler, Christoph and
	Schmidt-Thieme, Lars},
  title = {Fast context-aware recommendations with factorization machines},
  booktitle = {Proceedings of the 34th international ACM SIGIR conference on Research
	and development in Information Retrieval},
  year = {2011},
  pages = {635--644},
  organization = {ACM}
}

@INPROCEEDINGS{Rennie2003,
  author = {Jason Rennie},
  title = {Automatic Feature Induction for Text Classification},
  booktitle = {Proceedings of the Spam Conference},
  year = {2003},
  note = {Available: \url{http://people.csail.mit.edu/~jrennie/talks/spam03.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://people.csail.mit.edu/~jrennie/talks/spam03.pdf}
}

@ARTICLE{article,
  author = {Rettinger, Achim and Lasch, Uta and Tresp, Volker and d'Amato,
	Claudia and Fanizzi, Nicola},
  title = {Mining the Semantic Web - Statistical learning for next generation
	knowledge bases},
  year = {2012},
  month = {01},
  booktitle = {DATA MINING AND KNOWLEDGE DISCOVERY},
  owner = {oschulte},
  timestamp = {2017.10.19}
}

@ARTICLE{Reynolds1970,
  author = {J.C. Reynolds},
  title = {{Transformational Systems and the Algebraic Structure of Atomic Formulas}},
  journal = {Machine Intelligence},
  year = {1970},
  volume = {5},
  pages = {135--171},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Riahi2015,
  author = {Riahi, Fatemeh and Schulte, Oliver},
  title = {Model-Based Outlier Detection for Object-Relational Data},
  booktitle = {2015 IEEE Symposium Series on Computational Intelligence},
  year = {2015},
  pages = {1590--1598},
  organization = {IEEE}
}

@INPROCEEDINGS{FatemehRiahi2013a,
  author = {Fatemeh Riahi and Oliver Schulte and Qing Li},
  title = {Identifying Important Nodes in Relational Data},
  booktitle = {AAAI Late Breaking Paper Track},
  year = {2013},
  pages = {116-118},
  file = {:/local-scratch/SRiahi/texmf/OliverSchulte2013.pdf:PDF},
  owner = {sriahi},
  timestamp = {2013.08.20},
  url = {http://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/viewFile/7126/6735}
}

@MISC{Riahi2014,
  author = {Fatemeh Riahi and Oliver Schulte and Qing Liang},
  title = {A Proposal for Statistical Outlier Detection in Relational Structures},
  howpublished = {AAAI-StarAI Workshop on Statistical-Relational AI},
  month = {July},
  year = {2014},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@INPROCEEDINGS{ribeiro2016should,
  author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  title = {Why should {I} trust you?: Explaining the predictions of any classifier},
  booktitle = {Proceedings SIGKDD},
  year = {2016},
  pages = {1135--1144},
  organization = {ACM}
}

@TECHREPORT{Richardson2003,
  author = {Matthew Richardson},
  title = {Learning in First-Order Probabilistic Representations},
  institution = {University of Washington, Department of CSE},
  year = {2003},
  type = {Ph.D. General Examination},
  number = {Technical Report \#03-10-01},
  keywords = {statistical-relational},
  owner = {oliverschulte},
  review = {nice survey on KBMC etc. Nice example of Ph.D. depth exam.},
  timestamp = {2010.07.19}
}

@INPROCEEDINGS{Riedel2013,
  author = {Sebastian Riedel and Limin Yao and Andrew McCallum and Benjamin M.
	Marlin},
  title = {Relation Extraction with Matrix Factorization and Universal Schemas},
  booktitle = {Human Language Technologies-NAACL},
  year = {2013},
  pages = {74--84},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.uni-trier.de/rec/bib/conf/naacl/RiedelYMM13},
  timestamp = {Fri, 21 Feb 2014 14:03:41 +0100},
  url = {http://aclweb.org/anthology/N/N13/N13-1008.pdf}
}

@INPROCEEDINGS{riedmiller2005neural,
  author = {Riedmiller, Martin},
  title = {Neural fitted {Q} iteration--first experiences with a data efficient
	neural reinforcement learning method},
  booktitle = {ECML},
  year = {2005},
  pages = {317--328},
  organization = {Springer}
}

@INPROCEEDINGS{Rigoutsos2004,
  author = {Isidore Rigoutsos and Tien Huynh},
  title = {Chung-Kwei: a Pattern-discovery-based System for the Automatic Identification
	of Unsolicited E-mail Messages (SPAM)},
  booktitle = {Proceedings of the First Conference on Email and Anti-Spam (CEAS)},
  year = {2004},
  note = {Available: \url{http://www.ceas.cc/papers-2004/153.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.ceas.cc/papers-2004/153.pdf}
}

@INPROCEEDINGS{Rios2004,
  author = {Gordon Rios and Hongyuan Zha},
  title = {Exploring Support Vector Machines and Random Forests for Spam Detection},
  booktitle = {Proceedings of the First Conference on Email and Anti-Spam (CEAS)},
  year = {2004},
  note = {Available: \url{http://www.ceas.cc/papers-2004/174.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.ceas.cc/papers-2004/174.pdf}
}

@BOOK{Rissanen1989,
  title = {Stochastic Complexity in Statistical Inquiry Theory},
  publisher = {World Scientific Publishing Co., Inc.},
  year = {1989},
  author = {Jorma Rissanen},
  address = {River Edge, NJ, USA},
  isbn = {9971508591},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Rissanen1978,
  author = {J. Rissanen},
  title = {Modeling by shortest data description},
  journal = {Automatica},
  year = {1978},
  pages = {465--471},
  key = {14},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Rocktaschel2015,
  author = {Rockt{\"a}schel, Tim and Singh, Sameer and Riedel, Sebastian},
  title = {Injecting logical background knowledge into embeddings for relation
	extraction},
  booktitle = {Proceedings of the 2015 Human Language Technology Conference of the
	North American Chapter of the Association of Computational Linguistics},
  year = {2015}
}

@ARTICLE{Roos2005,
  author = {Roos, Teemu and Wettig, Hannes and Gr\"{u}nwald, Peter and Myllym\"{a}ki,
	Petri and Tirri, Henry},
  title = {On Discriminative Bayesian Network Classifiers and Logistic Regression},
  journal = {Mach. Learn.},
  year = {2005},
  volume = {59},
  pages = {267--296},
  number = {3},
  address = {Hingham, MA, USA},
  doi = {http://dx.doi.org.proxy.lib.sfu.ca/10.1007/s10994-005-0471-6},
  file = {Roos2005.pdf:Roos2005.pdf:PDF},
  issn = {0885-6125},
  keywords = {{B}ayes Nets},
  publisher = {Kluwer Academic Publishers},
  review = {discusses logistic regression and Bayes net classifiers. Mentions
	that under certain conditions, logistic regression parameters have
	global optimum.}
}

@INPROCEEDINGS{bib:langley-chem2,
  author = {Rose, D. and Langley, P.},
  title = {A hill-climbing approach to machine discovery},
  booktitle = {Proceedings of the 5th International Conference On Machine Learning},
  year = {1988},
  pages = {367--373},
  address = {Ann Arbor, MI},
  publisher = {Morgan Kaufmann},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{bib:langley-chem1,
  author = {Rose, D. and Langley, P.},
  title = {Chemical Discovery as Belief Revision},
  journal = {Machine Learning},
  year = {1986},
  volume = {1},
  pages = {423--452},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ELECTRONIC{Rosenbaum2004,
  author = {Dan T. Rosenbaum},
  month = {April},
  year = {2004},
  title = {Measuring How NBA Players Help Their Teams Win},
  language = {English},
  url = {http://www.82games.com/comm30.htm},
  keywords = {sports},
  owner = {Kurt},
  timestamp = {2015.01.20}
}

@BOOK{bib:game-convention,
  title = {Rules of Encounter: Designing Conventions for Automated Negotiation
	among Computers},
  publisher = {MIT Press},
  year = {1994},
  author = {J.S. Rosenschein and G. Zlotkin},
  address = {Cambridge, Mass.},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Rossi2015,
  author = {Rossi, Ryan A and Ahmed, Nesreen K},
  title = {Role discovery in networks},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2015},
  volume = {27},
  pages = {1112--1131},
  number = {4},
  publisher = {IEEE}
}

@ARTICLE{bib:rott,
  author = {Hans Rott},
  title = {Two Dogmas of Belief Revision},
  journal = {Journal of Philosophy},
  year = {2000},
  volume = {97},
  pages = {503--22},
  number = {9},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{bib:rott-tark,
  author = {Hans Rott},
  title = {Logic and Choice},
  booktitle = {Tark 98: Proceedings of the Seventh Conference on Theoretical Aspects
	of Rationality and Knowledge},
  year = {1998},
  pages = {235-48},
  address = {San Francisco},
  publisher = {Kaufmann},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{bib:rott-recover,
  author = {Hans Rott and Sven Ove Hansson},
  title = {Beyond Recovery? A Reply to Tennant},
  journal = {Erkenntnis},
  year = {1998},
  volume = {49},
  pages = {387--392},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@MASTERSTHESIS{Routley2015,
  author = {Kurt Routley},
  title = {A {Markov} Game Model for Valuing Player Actions in Ice Hockey},
  school = {Simon Fraser University},
  year = {2015},
  month = {April},
  abstract = {Evaluating player actions is very important for general managers and
	coaches in the National Hockey League. Researchers have developed
	a variety of advanced statistics to assist general managers and coaches
	in evaluating player actions. These advanced statistics fail to account
	for the context in which an action occurs or to look ahead to the
	long-term effects of an action. I apply the Markov Game formalism
	to play-by-play events recorded in the National Hockey League to
	develop a novel approach to valuing player actions. The Markov Game
	formalism incorporates context and lookahead across play-by-play
	sequences. A dynamic programming algorithm for value iteration learns
	the values of Q-functions in different states of the Markov Game
	model. These Q-values quantify the impact of actions on goal scoring,
	receiving penalties, and winning games. Learning is based on a massive
	dataset that contains over 2.8 million events in the National Hockey
	League. The impact of player actions varies widely depending on the
	context, with possible positive and negative effects for the same
	action. My results show using context features and lookahead makes
	a substantial difference to the action impact scores. Accounting
	for context and lookahead also increases the information in the model.
	Players are ranked according to the aggregate impact of their actions,
	and compared with previous player metrics, such as plus-minus, total
	points, and salary, as well as recent advanced statistics metrics.},
  file = {:Routley2015.pdf:PDF},
  keywords = {Markov Game model; ice hockey; value iteration; player ranking;},
  owner = {Kurt},
  timestamp = {2015.05.01}
}

@INPROCEEDINGS{Routley2015a,
  author = {Kurt Routley and Oliver Schulte},
  title = {A {Markov} Game Model for Valuing Player Actions in Ice Hockey},
  booktitle = {Uncertainty in Artificial Intelligence (UAI)},
  year = {2015},
  pages = {782--791},
  owner = {oschulte},
  timestamp = {2015.04.09}
}

@MISC{bib:sports-site,
  author = {Kurt Routley and Oliver Schulte and Zeyu Zhao},
  title = {Repository for {Markov} Game Models in Sports: Datsets and Models},
  howpublished = {\url{http://www.cs.sfu.ca/~oschulte/sports/}},
  month = {June},
  year = {2015},
  owner = {oschulte},
  timestamp = {2015.06.01}
}

@BOOK{bib:royden,
  title = {Real Analysis},
  publisher = {Macmillan},
  year = {1988},
  author = {H. L. Royden},
  address = {New York},
  edition = {3},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Russell2015,
  author = {Russell, Stuart},
  title = {Unifying logic and probability},
  journal = {Communications of the ACM},
  year = {2015},
  volume = {58},
  pages = {88--97},
  number = {7},
  abstract = {for file see slack},
  keywords = {statistical-relational},
  publisher = {ACM}
}

@BOOK{Russell2010,
  title = {Artificial Intelligence: A Modern Approach},
  publisher = {Prentice Hall},
  year = {2010},
  author = {Stuart Russell and Peter Norvig},
  owner = {oschulte},
  timestamp = {2011.09.30}
}

@PHDTHESIS{Russell1986,
  author = {S. J. Russell},
  title = {Analogical and Inductive Reasoning},
  school = {Stanford University},
  year = {1986},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@TECHREPORT{Ryder,
  author = {Alan Ryder},
  title = {Poisson Toolbox, a review of the application of the Poisson Probability
	Distribution in hockey},
  institution = {Hockey Analytics},
  year = {2004},
  url = {http://hockeyanalytics.com/Research_files/Poisson_Toolbox.pdf}
}

@INPROCEEDINGS{Sahami1998,
  author = {Mehran Sahami and Susan Dumais and David Heckerman and Eric Horvitz},
  title = {A {B}ayesian Approach to Filtering Junk E-Mail},
  booktitle = {Learning for Text Categorization: Papers from the 1998 Workshop},
  year = {1998},
  address = {Madison, Wisconsin},
  publisher = {AAAI Technical Report WS-98-05},
  note = {Available: \url{http://robotics.stanford.edu/users/sahami/papers-dir/spam.ps}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://robotics.stanford.edu/users/sahami/papers-dir/spam.ps}
}

@INPROCEEDINGS{Sakai2013,
  author = {Sakai, Yoshiki and Yamanishi, Kenji},
  title = {An {NML}-based model selection criterion for general relational data
	modeling},
  booktitle = {Big Data, 2013 IEEE International Conference on},
  year = {2013},
  pages = {421--429},
  organization = {IEEE},
  owner = {oschulte},
  timestamp = {2016.04.06}
}

@INPROCEEDINGS{Sakkis2001,
  author = {Georgios Sakkis and Ion Androutsopoulos and Georgios Paliouras and
	Vangelis Karkaletsis and Constantine D. Spyropoulos and Panagiotis
	Stamatopoulos},
  title = {Stacking Classifiers for Anti-Spam Filtering of E-Mail},
  booktitle = {Proceedings of EMNLP-01, 6th Conference on Empirical Methods in Natural
	Language Processing},
  year = {2001},
  address = {Pittsburgh, US},
  publisher = {Association for Computational Linguistics, Morristown, US},
  note = {Available: \url{http://www.arxiv.org/abs/cs.CL/0106040}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.arxiv.org/abs/cs.CL/0106040}
}

@ARTICLE{Sakkis2003,
  author = {G. Sakkis and I. Androutsopoulos and G. Paliouras and V. Karkaletsis
	and {C. D.} Spyropoulos and P. Stamatopoulos},
  title = {A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists},
  journal = {Information Retrieval Journal},
  year = {2003},
  volume = {6},
  number = {1},
  note = {Available: \url{http://www.eden.rutgers.edu/~gsakkis/docs/IR2003.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.eden.rutgers.edu/~gsakkis/docs/IR2003.pdf}
}

@INPROCEEDINGS{Salib2003,
  author = {Michael Salib},
  title = {Heuristics in the Blender},
  booktitle = {Proceedings of the Spam Conference},
  year = {2003},
  note = {Available: \url{http://web.mit.edu/msalib/www/writings/talks/spam-filtering-conference/spam-presentation.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = { http://web.mit.edu/msalib/www/writings/talks/spam-filtering-conference/spam-presentation.pdf}
}

@ARTICLE{bib:salmon,
  author = {Wesley Salmon},
  title = {Hans Reichenbach's Vindication of Induction},
  journal = {Erkenntnis},
  year = {1991},
  volume = {35},
  pages = {99-122},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@BOOK{Salunke2014,
  title = {Selenium Webdriver in Java: Learn With Examples},
  publisher = {CreateSpace Independent Publishing Platform},
  year = {2014},
  author = {Salunke, Sagar Shivaji},
  address = {USA},
  edition = {1st},
  owner = {Kurt},
  timestamp = {2014.12.22}
}

@INPROCEEDINGS{Sammut:1992:LF:141975.142067,
  author = {Sammut, Claude and Hurst, Scott and Kedzier, Dana and Michie, Donald},
  title = {{Learning to Fly}},
  booktitle = {Proceedings of the Ninth International Workshop on Machine Learning},
  year = {1992},
  series = {ML92},
  pages = {385--393},
  address = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann Publishers Inc.},
  acmid = {142067},
  isbn = {1-5586-247-X},
  location = {Aberdeen, Scotland, United Kingdom},
  numpages = {9},
  owner = {oschulte},
  timestamp = {2018.06.02},
  url = {http://dl.acm.org/citation.cfm?id=141975.142067}
}

@INPROCEEDINGS{Sankaranarayanan2014,
  author = {Sankaranarayanan, Vignesh Veppur and Sattar, Junaed and Lakshmanan,
	Laks VS},
  title = {Auto-play: A data mining approach to ODI cricket simulation and prediction},
  booktitle = {2014 SIAM International Conference on Data Mining},
  year = {2014},
  pages = {1064--1072},
  keywords = {sports}
}

@ARTICLE{Sarajlic2016,
  author = {Sarajli{\'c}, Anida and Malod-Dognin, No{\"e}l and Yavero{\ug}lu,
	{\"O}mer Nebil and Pr{\vz}ulj, Nata{\vs}a},
  title = {Graphlet-based Characterization of Directed Networks},
  journal = {Nature: Scientific Reports},
  year = {2016},
  volume = {6},
  pages = {35098 EP -},
  month = {10},
  bdsk-url-1 = {http://dx.doi.org/10.1038/srep35098},
  date = {2016/10/13/online},
  date-added = {2017-02-17 18:39:51 +0000},
  date-modified = {2017-02-17 18:39:51 +0000},
  day = {13},
  l3 = {10.1038/srep35098; http://www.nature.com/articles/srep35098#supplementary-information},
  m3 = {Article},
  publisher = {The Author(s) SN -},
  ty = {JOUR},
  url = {http://dx.doi.org/10.1038/srep35098}
}

@ARTICLE{Sarma2009,
  author = {Sarma, Anish Das and Benjelloun, Omar and Halevy, Alon and Nabar,
	Shubha and Widom, Jennifer},
  title = {Representing Uncertain Data: Models, Properties, and Algorithms},
  journal = {The VLDB Journal},
  year = {2009},
  volume = {18},
  pages = {989--1019},
  number = {5},
  month = oct,
  acmid = {1644250},
  address = {Secaucus, NJ, USA},
  doi = {10.1007/s00778-009-0147-0},
  issn = {1066-8888},
  issue_date = {October 2009},
  keywords = {Data modeling, Uncertain data, Uncertainty},
  numpages = {31},
  owner = {zqian},
  publisher = {Springer-Verlag New York, Inc.},
  timestamp = {2014.09.16},
  url = {http://dx.doi.org/10.1007/s00778-009-0147-0}
}

@ARTICLE{Sato1995,
  author = {Masako Sato},
  title = {Inductive inference of formal languages},
  journal = {Bulletins of informatics and cybernetics},
  year = {1995},
  volume = {27},
  pages = {85-106},
  number = {1},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@BOOK{bib:savage,
  title = {The Foundations of Statistics},
  publisher = {Dover},
  year = {1954},
  author = {Leonard Savage},
  address = {New York},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@BOOK{Schoelkopf2001,
  title = {Learning with Kernels Support Vector Machines, Regularization, Optimization,
	and Beyond},
  publisher = {The MIT Press},
  year = {2001},
  author = {Bernhard Sch????lkopf and Alexander J. Smola},
  owner = {hkhosrav},
  timestamp = {2009.02.20}
}

@MANUAL{Scheines1996,
  title = {TETRAD 3:Tools for Causal Modeling},
  author = {R. Scheines and P. Spirtes and C. Glymour and C. Meek and T. Richardson},
  organization = {CMU Philosopy},
  year = {1996},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Schmidt2010,
  author = {Schmidt, M. and Murphy, K.},
  title = {Convex structure learning in log-linear models: Beyond pairwise potentials},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence
	and Statistics (AISTATS)},
  year = {2010},
  pages = {??},
  review = {We use hierarchical inclusion restriction wrt edges. We use lattice
	search on edges. But not on potentials (for future work) Foreign
	key constraints: valid relationship sets are smaller than full powerset.
	It's like active set search but with extra constraints (attributes
	from different tables are independent unless conditioned on links)
	More precise version: we have a context-dependent hierarchy inclusion
	property: If w(A)|C = 0 and A \subset B, C \subset D, then w(B)|D
	= 0 We do a full search over possible contexts, rather than an active
	set search. For a given context, we apply the inclusion property.
	We could also have an inclusion property within a given context.
	That would facilitate a hierarchy search both within and across contexts.}
}

@INPROCEEDINGS{bib:kevinl1,
  author = {M Schmidt and A Niculescu-Mizil and K Murphy},
  title = {Learning Graphical Model Structure using {L1}-Regularization Path},
  booktitle = {AAAI},
  year = {2007},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Schneider2004,
  author = {Karl-Michael Schneider},
  title = {Learning to Filter Junk E-Mail from Positive and Unlabeled Examples},
  booktitle = {Proceedings of the 1st International Joint Conference on Natural
	Language Processing {(IJCNLP}-04), Sanya City, Hainan Island, China},
  year = {2004},
  pages = {602--607},
  note = {Available: \url{http://www.phil.uni-passau.de/linguistik/mitarbeiter/schneider/pub/ijcnlp2004.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = { http://www.phil.uni-passau.de/linguistik/mitarbeiter/schneider/pub/ijcnlp2004.pdf}
}

@INPROCEEDINGS{Schneider2003,
  author = {{Karl-Michael} Schneider},
  title = {A Comparison of Event Models for Naive {B}ayes Anti-Spam E-Mail Filtering},
  booktitle = {Proceedings of the 11th Conference of the European Chapter of the
	Association for Computational Linguistics {(EACL'03)}},
  year = {2003},
  note = {Available: \url{http://www.phil.uni-passau.de/linguistik/mitarbeiter/schneider/pub/eacl2003.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = { http://www.phil.uni-passau.de/linguistik/mitarbeiter/schneider/pub/eacl2003.pdf}
}

@CONFERENCE{Schucker2013,
  author = {Michael Schuckers and James Curro},
  title = {Total Hockey Rating (THoR): A comprehensive statistical rating of
	National Hockey League forwards and defensemen based upon all on-ice
	events},
  booktitle = {MIT sloan Sports Analytics Conference},
  year = {2013}
}

@INPROCEEDINGS{Schuckers2013,
  author = {Michael Schuckers and James Curro},
  title = {Total Hockey Rating (THoR): A comprehensive statistical rating of
	National Hockey League forwards and defensemen based upon all on-ice
	events},
  booktitle = {7th Annual MIT Sloan Sports Analytics Conference},
  year = {2013},
  file = {:Schuckers2013 - Total Hockey Rating THoR A comprehensive statistical rating of National Hockey League forwards and defensemen based upon all on-ice events.pdf:PDF},
  owner = {oschulte},
  timestamp = {2014.06.05}
}

@UNPUBLISHED{Schuckers2011,
  author = {Michael E. Schuckers and Dennis F. Lock and Chris Wells and C. J.
	Knickerbocker and Robin H. Lock},
  title = {National Hockey League Skater Ratings Based upon All On-Ice Events:
	An Adjusted Minus/Plus Probability (AMPP) Approach},
  note = {Unpublished manuscript.},
  year = {2011},
  file = {:Schuckers2011 - National Hockey League Skater Ratings Based upon All On-Ice Events An Adjusted MinusPlus Probability AMPP Approach.pdf:PDF},
  owner = {Kurt},
  timestamp = {2014.04.18},
  url = {http://myslu.stlawu.edu/~msch/sports/LockSchuckersProbPlusMinus113010.pdf}
}

@CONFERENCE{Schuckers2012,
  author = {{Michael} Schuckers and {Lauren} Brozowski},
  title = {Referee Analytics: An Analysis of Penalty Rates by National Hockey
	League Officials},
  booktitle = {MIT Sloan Sports Analytics Conference},
  year = {2012},
  file = {:Schuckers2012.pdf:PDF},
  owner = {Kurt},
  timestamp = {2015.01.28}
}

@ARTICLE{Schuckers2014a,
  author = {{Schuckers}, M. and {Argeris}, S.},
  title = {{You Can Beat the Market: Estimating the Return on Investment for
	National Hockey League (NHL) Team Scouting using a Draft Value Pick
	Chart for the NHL}},
  journal = {ArXiv e-prints},
  year = {2014},
  month = nov,
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1411.5754S},
  archiveprefix = {arXiv},
  eprint = {1411.5754},
  file = {:Schuckers2014a.pdf:PDF},
  keywords = {Statistics - Applications},
  owner = {Kurt},
  primaryclass = {stat.AP},
  timestamp = {2015.01.25}
}

@ARTICLE{Schuckers2014,
  author = {{Schuckers}, M. and {Macdonald}, B.},
  title = {Accounting for Rink Effects in the National Hockey League's Real
	Time Scoring System},
  journal = {ArXiv e-prints},
  year = {2014},
  month = {dec},
  file = {:Shuckers2014.pdf:PDF},
  keywords = {Statistics - Applications},
  owner = {Kurt},
  timestamp = {2015.01.25}
}

@INCOLLECTION{Schulte2007,
  author = {Oliver Schulte},
  title = {Logically Reliable Inductive Inference},
  booktitle = {Induction, Algorithmic Learning Theory, and Philosophy},
  publisher = {Dordrecht: Springer},
  year = {2007},
  editor = {Michele Friend and Norma B. Goethe and Valentina S. Harizanov},
  volume = {9},
  series = {Logic, Epistemology, and the Unity of Science},
  pages = {157--178},
  owner = {oschulte},
  timestamp = {2009.01.15}
}

@INPROCEEDINGS{Schulte07,
  author = {Oliver Schulte},
  title = {How Particle Phyics Cut Nature At Its Joints},
  booktitle = {13th International Congress on Logic, Methodology and the Philosophy
	of Science (LMPS), Beijing, China.},
  year = {2009},
  editor = {Glymour, Wei and Westerst{\aa}hl},
  pages = {267--286},
  publisher = {College Publications, London.},
  note = {Invited Lecture.},
  owner = {oliverschulte},
  timestamp = {2009.01.19}
}

@INCOLLECTION{Schulte2006,
  author = {Oliver Schulte},
  title = {How Do the {H}arper and {L}evi Identities Constrain Belief Change?},
  booktitle = {Truth and Probability: Essays in Honour of Hugh LeBlanc},
  publisher = {College Publications, London.},
  year = {2006},
  editor = {Francois LePage and Bryson Brown},
  pages = {123--137},
  keywords = {oliver},
  owner = {oschulte},
  timestamp = {2009.01.15}
}

@INCOLLECTION{Schulte2005b,
  author = {Schulte, Oliver},
  title = {Formal Learning Theory},
  booktitle = {The Stanford Encyclopedia of Philosophy},
  publisher = {Stanford University},
  year = {2005},
  editor = {Edward N. Zalta},
  edition = {Summer 2005 Edition},
  owner = {oliverschulte},
  timestamp = {2008.12.30},
  url = {http://plato.stanford.edu/archives/sum2005/entries/learning-formal/}
}

@ARTICLE{Schulte2018,
  author = {Schulte, Oliver},
  title = {Causal Learning with Occam's Razor},
  journal = {Studia Logica},
  year = {2018},
  volume = {Special Issue},
  pages = {1-33},
  month = {Sep},
  day = {06},
  doi = {10.1007/s11225-018-9829-1},
  issn = {1572-8730},
  url = {https://doi.org/10.1007/s11225-018-9829-1}
}

@MISC{Schulte2012c,
  author = {Oliver Schulte},
  title = {Challenge Paper: Marginal Probabilities for Instances and Classes},
  howpublished = {ICML-SRL Workshop on Statistical Relational Learning.},
  month = {June},
  year = {2012},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@INPROCEEDINGS{Schulte2011,
  author = {Oliver Schulte},
  title = {A tractable pseudo-likelihood function for {Bayes} Nets applied to
	relational data},
  booktitle = {SIAM SDM},
  year = {2011},
  pages = {462-473},
  file = {Schulte2011.pdf:Schulte2011.pdf:PDF},
  keywords = {oliver, statistical-relational},
  owner = {oliverschulte},
  timestamp = {2011.01.13}
}

@TECHREPORT{Schulte2010c,
  author = {Oliver Schulte},
  title = {Measuring Data Fit for {Bayes} Nets Applied to Relational Datasets},
  institution = {School of Computing Science, Simon Fraser University},
  year = {2010},
  number = {TR 2010-09},
  month = {August},
  abstract = {Bayes nets (BNs) for relational databases are a major research topic
	in machine learning and artificial intelligence. When cyclic dependencies
	exist, measuring the fit of a BN model to relational data with a
	likelihood function is a challenge \cite{Domingos2007,Taskar2002,bib:jensen-chapter,Getoor2007c}.
	A common approach to difficulties in defining a likelihood function
	is to employ a pseudo-likelihood; a prominent example is the pseudo
	likelihood defined for Markov Logic Networks (MLNs). This paper proposes
	a new pseudo likelihood $P^{\pseudo}$ for Parametrized Bayes Nets
	(PBNs) \cite{Poole2003}. The pseudo log-likelihood $L^{\pseudo} =
	ln(P^{\pseudo})$ is similar to the single-table BN log-likelihood,
	where row counts in the data table are replaced by frequencies in
	the database. We introduce a new type of semantics based on the concept
	of random instantiations (groundings) from classic AI research \cite{Halpern90,Bacchus90}:
	The measure $L^{\pseudo}$ is the expected log-likelihood of a random
	instantiation of the 1st-order variables in the PBN. The standard
	moralization method for converting a PBN to an MLN provides another
	interpretation of $L^{\pseudo}$: the measure is closely related to
	the log-likelihood and to the pseudo log-likelihood of the moralized
	PBN. For parameter learning, the $L^{\pseudo}$-maximizing estimates
	are the empirical conditional frequencies in the databases. For structure
	learning, we show that the state of the art learn-and-join method
	of Khosravi {\em et al.} \cite{Khosravi2010} implicitly maximizes
	the $L^{\pseudo}$ measure. The measure provides a theoretical foundation
	for this algorithm, while the algorithm's empirical success provides
	experimental validation for its usefulness.},
  file = {Schulte2010c.pdf:Schulte2010c.pdf:PDF},
  keywords = {statistical-relational},
  org = {SFU-CMPT},
  pages = {17},
  url = {http://www.cs.sfu.ca/research/publications/techreports/}
}

@INPROCEEDINGS{Schulte2009a,
  author = {Oliver Schulte},
  title = {Simultaneous Discovery of Conservation Laws and Hidden Particles
	With Smith Matrix Decomposition},
  booktitle = {Proceedings of the Twenty-First International Joint Conference on
	Artificial Intelligence (IJCAI-09)},
  year = {2009},
  pages = {1481--1487},
  note = {Nominated for best paper award.},
  file = {Schulte09.pdf:Schulte09.pdf:PDF},
  keywords = {particle physics, oliver},
  owner = {oliverschulte},
  timestamp = {2009.07.19}
}

@ARTICLE{Schulte08a,
  author = {Oliver Schulte},
  title = {{Review of {\em Reliable Reasoning: Induction and Statistical Learning
	Theory}, by G. {H}arman and S. {K}ulkarni}},
  journal = {Biometrics},
  year = {2008},
  volume = {64},
  pages = {992-993},
  number = {3},
  month = {September},
  owner = {oliverschulte},
  timestamp = {2009.01.19}
}

@ARTICLE{Schulte08b,
  author = {Oliver Schulte},
  title = {The Co-Discovery of Conservation Laws and Particle Families},
  journal = {Studies in the History and Philosophy of Modern Physics},
  year = {2008},
  volume = {39},
  pages = {288-314},
  number = {2},
  file = {Schulte08b.pdf:Schulte08b.pdf:PDF},
  keywords = {linear algebra, particle physics},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@MISC{Schulte2007b,
  author = {Oliver Schulte},
  title = {Data Sets and Programs for Particle Reaction Analysis},
  howpublished = {URL = http://www.cs.sfu.ca/$\sim$oschulte/particles/},
  year = {2007},
  owner = {oschulte},
  timestamp = {2009.01.07},
  url = {http://www.cs.sfu.ca/$\sim$oschulte/particles/}
}

@INPROCEEDINGS{Schulte2007f,
  author = {Oliver Schulte},
  title = {How Particle Phyics Cut Nature At Its Joints},
  booktitle = {13th International Congress on Logic, Methodology and the Philosophy
	of Science (LMPS), Beijing, China.},
  year = {2007},
  note = {Invited Lecture.},
  owner = {oliverschulte},
  timestamp = {2009.01.19}
}

@MISC{Schulte2006o,
  author = {O. Schulte},
  title = {How Do the Harper and Levi Identities Constrain Belief Change?},
  year = {2006},
  booktitle = {Truth and Probability, Essays in Honour of Hugh LeBlanc, eds. Francois
	LePage and Bryson Brown},
  owner = {oschulte},
  pages = {123--137},
  publisher = {College Publications, London}
}

@UNPUBLISHED{Schulte2005,
  author = {Oliver Schulte},
  title = {Automated Discovery of Conservation Principles and New Particles
	in Particle Physics},
  note = {Manuscript Under Review},
  year = {2005},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{schulte_minimal_2002,
  author = {O. Schulte},
  title = {Minimal belief change, {P}areto-optimality and logical consequence},
  journal = {Economic Theory},
  year = {2002},
  volume = {19},
  pages = {105--144},
  number = {1}
}

@ARTICLE{bib:review,
  author = {Oliver Schulte},
  title = {{Review of {\em Elements of Scientific Inquiry}, by E. {M}artin and
	D. {O}sherson}},
  journal = {The British Journal for the Philosophy of Science},
  year = {2000},
  volume = {51},
  pages = {347-352},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Schulte2000,
  author = {O. Schulte},
  title = {Inferring Conservation Laws in Particle Physics: A Case Study in
	the Problem of Induction},
  journal = {The British Journal for the Philosophy of Science},
  year = {2000},
  volume = {51(4)},
  pages = {771-806},
  month = {December},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{schulte_discussion,
  author = {O. Schulte},
  title = {Discussion. {W}hat to believe and what to take seriously: a reply
	to {David Chart} concerning the riddle of induction},
  journal = {The British Journal for the Philosophy of Science},
  year = {2000},
  volume = {51},
  pages = {151--153},
  number = {1}
}

@ARTICLE{bib:jpl,
  author = {Oliver Schulte},
  title = {The Logic of Reliable and Efficient Inquiry},
  journal = {The Journal of Philosophical Logic},
  year = {1999},
  volume = {28},
  pages = {399-438},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{bib:synthese,
  author = {Oliver Schulte},
  title = {Minimal Belief Change and the {P}areto Principle},
  journal = {Synthese},
  year = {1999},
  volume = {118},
  pages = {329-361},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{bib:bjps,
  author = {Oliver Schulte},
  title = {Means-Ends Epistemology},
  journal = {The British Journal for the Philosophy of Science},
  year = {1996},
  volume = {79},
  pages = {141--147},
  number = {1},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{OliverSchulte2013,
  author = {Oliver Schulte and Bahareh Bina and Branden Crawford and Derek Bingham
	and Yi Xiong},
  title = {A Hierarchy of Independence Assumptions for Multi-relational Bayes
	Net Classifiers},
  booktitle = {IEEE SSCI Symposium on Computational Intelligence},
  year = {2013},
  pages = {150-159},
  owner = {oschulte},
  timestamp = {2013.01.14}
}

@ARTICLE{DBLP:journals/amai/SchulteD04,
  author = {Oliver Schulte and James P. Delgrande},
  title = {Representing von {N}eumann-{M}orgenstern Games in the Situation Calculus},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year = {2004},
  volume = {42},
  pages = {73-101},
  number = {1-3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://www.springerlink.com/index/10.1023/B:AMAI.0000034523.18162.6b},
  keywords = {oliver}
}

@INPROCEEDINGS{oschulte2010md,
  author = {Oliver Schulte and Mark S. Drew},
  title = {Learning Conservation Laws Via Matrix Search},
  booktitle = {Proceedings of the 13th Conference on Discovery Science},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {235-250},
  publisher = {Springer},
  owner = {oliverschulte}
}

@ARTICLE{Schulte2007a,
  author = {Oliver Schulte and Mark S. Drew},
  title = {Algorithmic Derivation of Additive Selection Rules and Particle Families
	from Reaction Data},
  journal = {CoRR-HEP Preprint Archive},
  year = {2007},
  volume = {abs/hep-ph/0602011v2},
  howpublished = {HEP Preprint Archive, http://arxiv.org/abs/hep-ph/0602011},
  owner = {oschulte},
  timestamp = {2009.01.07},
  url = {http://arxiv.org/abs/hep-ph/0602011v2}
}

@TECHREPORT{Schulte2006a,
  author = {Oliver Schulte and Mark S. Drew},
  title = {An algorithmic proof that the family conservation laws are optimal
	for the current reaction data},
  institution = {School of Computing Science, Simon Fraser University},
  year = {2006},
  number = {2006-03},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Schulte2009,
  author = {Oliver Schulte and Gustavo Frigo and Russell Greiner and Hassan Khosravi},
  title = {A New Hybrid Method for {B}ayesian Network Learning With Dependency
	Constraints},
  booktitle = {Proceedings IEEE SSCI Symposium},
  year = {2009},
  pages = {53--60},
  note = {Symposium on Computational Intelligence and Data Mining},
  file = {Schulte2009.pdf:Schulte2009.pdf:PDF},
  keywords = {{B}ayes Nets, oliver},
  owner = {oschulte},
  timestamp = {2009.01.15}
}

@INPROCEEDINGS{Schulte2017a,
  author = {Oliver Schulte and Sajjad Gholami},
  title = {Locally Consistent {Bayesian} Network Scores for Multi-Relational
	Data},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence
	(IJCAI)},
  year = {2017},
  pages = {2693-2700},
  owner = {oschulte},
  timestamp = {2017.06.19},
  url = {\url{https://www.ijcai.org/proceedings/2017/0375.pdf}}
}

@ARTICLE{bib:monist,
  author = {Oliver Schulte and Cory Juhl},
  title = {Topology as Epistemology},
  journal = {The Monist},
  year = {1996},
  volume = {79},
  pages = {141--147},
  number = {1},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@MISC{Schulte2018am,
  author = {Oliver Schulte and Mahmoud Khademi},
  title = {Image Caption Generation with Hierarchical ContextualVisual Spatial
	Attention},
  year = {2018},
  booktitle = {Workshop Proceedings in CVPR}
}

@ARTICLE{Schulte2017,
  author = {Schulte, Oliver and Khademi, Mahmoud and Gholami, Sajjad and Zhao,
	Zeyu and Javan, Mehrsan and Desaulniers, Philippe},
  title = {A {Markov} Game model for valuing actions, locations, and team performance
	in ice hockey},
  journal = {Data Mining and Knowledge Discovery},
  year = {2017},
  pages = {1--23},
  abstract = {We apply the Markov Game formalism to develop a context-aware approach
	to valuing player actions, locations, and team performance in ice
	hockey. The Markov Game formalism uses machine learning and AI techniques
	to incorporate context and look-ahead. Dynamic programming is applied
	to learn value functions that quantify the impact of actions on goal
	scoring. Learning is based on a massive new dataset, from SportLogiq,
	that contains over 1.3M events in the National Hockey League. The
	SportLogiq data include the location of an action, which has previously
	been unavailable in hockey analytics. We give examples showing how
	the model assigns context and location aware values to a large set
	of 13 action types. Team performance can be assessed as the aggregate
	value of actions performed by the team's players, or the aggregate
	value of states reached by the team. Model validation shows that
	the total team action and state value both provide a strong indicator
	predictor of team success, as measured by the team's average goal
	ratio.},
  doi = {10.1007/s10618-017-0496-z},
  issn = {1573-756X},
  url = {http://dx.doi.org/10.1007/s10618-017-0496-z}
}

@ARTICLE{schulte2017markov,
  author = {Schulte, Oliver and Khademi, Mahmoud and Gholami, Sajjad and Zhao,
	Zeyu and Javan, Mehrsan and Desaulniers, Philippe},
  title = {A {M}arkov Game model for valuing actions, locations, and team performance
	in ice hockey},
  journal = {Data Mining and Knowledge Discovery},
  year = {2017},
  pages = {1--23},
  publisher = {Springer}
}

@ARTICLE{Schulte2012,
  author = {Oliver Schulte and Hassan Khosravi},
  title = {Learning graphical models for relational data via lattice search},
  journal = {Machine Learning},
  year = {2012},
  volume = {88},
  pages = {331-368},
  number = {3},
  owner = {oliverschulte},
  timestamp = {2012.04.11}
}

@INPROCEEDINGS{Schulte2009c,
  author = {Oliver Schulte and Hassan Khosravi and Bahareh Bina},
  title = {{B}ayes Nets for combining logical and probabilistic structure},
  booktitle = {Proceedings {STRUCK} Workshop on Learning Structural Knowledge From
	Observations},
  year = {2009},
  organization = {IJCAI-09},
  howpublished = {IJCAI-09 Workshop on Learning Structural Knowledge From Observations},
  keywords = {{B}ayes Nets, statistical-relational, oliver, digging},
  owner = {oschulte},
  timestamp = {2009.06.30},
  url = {http://www.cs.umd.edu/~ukuter/struck09/final-papers/11.pdf}
}

@INPROCEEDINGS{Schulte2009e,
  author = {Oliver Schulte and Hassan Khosravi and Bahareh Bina and Flavia Moser},
  title = {Join {B}ayes Nets: A New Type of {B}ayes net for Relational Data},
  booktitle = {Proceedings {GKR} Workshop on Graph Structures for Knowledge Representation
	and Reasoning},
  year = {2009},
  pages = {19-24},
  organization = {IJCAI-09},
  howpublished = {IJCAI-09 Workshop on Graph Structures for Knowledge Representation
	and Reasoning (GKR)},
  keywords = {{B}ayes Nets, statistical-relational, oliver, digging},
  owner = {oschulte},
  timestamp = {2009.06.30},
  url = {http://www.lirmm.fr/~croitoru/GKR/proceedings.html}
}

@MISC{Schulte2012d,
  author = {Oliver Schulte and Hassan Khosravi and Tianxiang Gao and Yuke Zhu},
  title = {Random Regression for Bayes Nets Applied to Relational Data.},
  howpublished = {UAI-StarAI Workshop on Statistical Relational AI},
  month = {August},
  year = {2012},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@ARTICLE{Schulte2014,
  author = {Oliver Schulte and Hassan Khosravi and Arthur Kirkpatrick and Tianxiang
	Gao and Yuke Zhu},
  title = {Modelling Relational Statistics With Bayes Nets},
  journal = {Machine Learning},
  year = {2014},
  volume = {94},
  pages = {105-125},
  owner = {zqian},
  timestamp = {2014.01.06},
  url = {http://link.springer.com/article/10.1007%2Fs10994-013-5362-7}
}

@INPROCEEDINGS{Schulte2012b,
  author = {Oliver Schulte and Hassan Khosravi and Arthur Kirkpatrick and Tianxiang
	Gao and Yuke Zhu},
  title = {Modelling Relational Statistics With Bayes Nets},
  booktitle = {Inductive Logic Programming (ILP)},
  year = {2012},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@MISC{Schulte2012e,
  author = {Oliver Schulte and Hassan Khosravi and Arthur Kirkpatrick and Tianxiang
	Gao and Yuke Zhu},
  title = {Modelling Relational Statistics With Bayes Nets},
  howpublished = {ICML-SRL Workshop on Statistical-Relational Learning},
  month = {June},
  year = {2012},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@ARTICLE{Schulte2012a,
  author = {Oliver Schulte and Hassan Khosravi and Tong Man},
  title = {Learning Directed Relational Models With Recursive Dependencies},
  journal = {Machine Learning},
  year = {2012},
  volume = {89},
  pages = {299-316},
  doi = {DOI 10.1007/s10994-012-5308-5},
  owner = {oliverschulte},
  timestamp = {2012.01.06}
}

@INPROCEEDINGS{Schulte2011b,
  author = {Oliver Schulte and Hassan Khosravi and Tong Man},
  title = {Learning Directed Relational Models With Recursive Dependencies},
  booktitle = {ILP},
  year = {2011},
  volume = {7207},
  series = {Springer LNAI},
  pages = {39-44},
  keywords = {statistical-relational, oliver},
  owner = {oliverschulte},
  timestamp = {2012.01.06}
}

@ARTICLE{Schulte09,
  author = {Oliver Schulte and Hassan Khosravi and Flavia Moser and Martin Ester},
  title = {Join Bayes Nets: A new type of Bayes net for relational data},
  journal = {CoRR-CS Learning Preprint Archive},
  year = {2009},
  volume = {abs/0811.4458},
  note = {updated version of Technical Report},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://arxiv.org/abs/0811.4458}
}

@MISC{Schulte2009ho,
  author = {O. Schulte and H. Khosravi and F. Moser and M. Ester},
  title = {Learning Class-Level Bayes Nets for Relational Data},
  year = {2009},
  booktitle = {CS-Learning Preprint Archive},
  owner = {oschulte}
}

@TECHREPORT{Schulte08,
  author = {Oliver Schulte and Hassan Khosravi and Flavia Moser and Martin Ester},
  title = {Join {B}ayes Nets: A new type of {B}ayes net for relational data.},
  institution = {Simon Fraser University},
  year = {2008},
  number = {2008-17},
  file = {Schulte08.pdf:Schulte08.pdf:PDF},
  keywords = {oliver, {B}ayes Nets, statistical-relational},
  owner = {oliverschulte},
  timestamp = {2009.01.19}
}

@ARTICLE{Schulte2010,
  author = {Oliver Schulte and Wei Luo and Russell Greiner},
  title = {Mind-change optimal learning of {B}ayes net structure from dependency
	and independency data},
  journal = {Information and Computation},
  year = {2010},
  volume = {208},
  pages = {63--82},
  file = {Schulte2010.pdf:Schulte2010.pdf:PDF},
  keywords = {{B}ayes Nets},
  owner = {oschulte},
  timestamp = {2009.11.12}
}

@ELECTRONIC{Schulte2015,
  author = {Oliver Schulte and Zhensong Qian},
  month = {August},
  year = {2015},
  title = {FactorBase: SQL for Learning A Multi-Relational Graphical Model},
  organization = {arXiv electronic preprint},
  owner = {oschulte},
  timestamp = {2015.08.14}
}

@MISC{Schulte2013a,
  author = {Oliver Schulte and Zhensong Qian},
  title = {Learning Bayes Nets for Relational Data With Link Uncertainty},
  howpublished = {IJCAI-HINA Workshop on Heterogeneous Information Network Analysis},
  month = {August},
  year = {2013},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@MISC{Schulte2013b,
  author = {Oliver Schulte and Zhensong Qian},
  title = {Learning Bayes Nets with Link Uncertainty for Relational Data Sets},
  howpublished = {IJCAI-AIBD Workshop on AI and Big Data},
  month = {August},
  year = {2013},
  owner = {oschulte},
  timestamp = {2012.07.24}
}

@ARTICLE{Schulte2016,
  author = {Schulte, Oliver and Qian, Zhensong and Kirkpatrick, Arthur E. and
	Yin, Xiaoqian and Sun, Yan},
  title = {Fast learning of relational dependency networks},
  journal = {Machine Learning},
  year = {2016},
  pages = {1--30},
  abstract = {A relational dependency network (RDN) is a directed graphical model
	widely used for multi-relational data. These networks allow cyclic
	dependencies, necessary to represent relational auto-correlations.
	We describe an approach for learning both the RDN's structure and
	its parameters, given an input relational database: First learn a
	Bayesian network (BN), then transform the Bayesian network to an
	RDN. Thus fast Bayesian network learning translates into fast RDN
	learning. The BN-to-RDN transform comprises a simple, local adjustment
	of the Bayesian network structure and a closed-form transform of
	the Bayesian network parameters. This method can learn an RDN for
	a dataset with a million tuples in minutes. We empirically compare
	our approach to a state-of-the-art RDN learning approach that applies
	functional gradient boosting, using six benchmark datasets. Learning
	RDNs via BNs scales much better to large datasets than learning RDNs
	with current boosting methods.},
  doi = {10.1007/s10994-016-5557-9},
  issn = {1573-0565},
  owner = {zqian},
  timestamp = {2016.03.31},
  url = {http://dx.doi.org/10.1007/s10994-016-5557-9}
}

@ARTICLE{Schulte2014a,
  author = {Oliver Schulte and Zhensong Qian and Arthur E. Kirkpatrick and Xiaoqian
	Yin and Yan Sun},
  title = {Fast Learning of Relational Dependency Networks},
  journal = {CoRR},
  year = {2014},
  volume = {abs/1410.7835},
  owner = {zqian},
  timestamp = {2014.10.30},
  url = {http://arxiv.org/abs/1410.7835}
}

@INPROCEEDINGS{Schulte2014b,
  author = {Schulte, Oliver and Routley, Kurt},
  title = {Aggregating predictions vs. aggregating features for relational classification},
  booktitle = {Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium
	on},
  year = {2014},
  pages = {121-128},
  month = {Dec},
  abstract = {Relational data classification is the problem of predicting a class
	label of a target entity given information about features of the
	entity, of the related entities, or neighbors, and of the links.
	This paper compares two fundamental approaches to relational classification:
	aggregating the features of entities related to a target instance,
	or aggregating the probabilistic predictions based on the features
	of each entity related to the target instance. Our experiments compare
	different relational classifiers on sports, financial, and movie
	data. We examine the strengths and weaknesses of both score and feature
	aggregation, both conceptually and empirically. The performance of
	a single aggregate operator (e.g., average) can vary widely across
	datasets, for both feature and score aggregation. Aggregate features
	can be adapted to a dataset by learning with a set of aggregate features.
	Used adaptively, aggregate features outperformed learning with a
	single fixed score aggregation operator. Since score aggregation
	is usually applied with a single fixed operator, this finding raises
	the challenge of adapting score aggregation to specific datasets.},
  doi = {10.1109/CIDM.2014.7008657},
  keywords = {Aggregates;Educational institutions;Games;Grounding;Logistics;Motion
	pictures;Standards},
  owner = {Kurt},
  timestamp = {2015.01.20}
}

@MISC{Schulte2015a,
  author = {Oliver Schulte and Zeyu Zhao, and Kurt Routley},
  title = {What is the Value of an Action in Ice Hockey? Learning a Q-function
	for the NHL},
  howpublished = {https://dtai.cs.kuleuven.be/events/MLSA15/papers/mlsa15_submission_14.pdf},
  month = {September},
  year = {2015},
  booktitle = {Machine Learning and Sports Analytics Workshop (@ECML)},
  owner = {oschulte},
  timestamp = {2015.04.09}
}

@INPROCEEDINGS{schulteapples,
  author = {Schulte, Oliver and Zhao, Zeyu and Javan, Mehrsan and Desaulniers,
	Philippe},
  title = {Apples-to-Apples: Clustering and Ranking NHL Players Using Location
	Information and Scoring Impact},
  booktitle = {Proceedings MIT Sloan Sports Analytics Conference},
  year = {2017},
  owner = {oschulte},
  timestamp = {2017.09.17}
}

@INPROCEEDINGS{Schwartz1993,
  author = {Schwartz, Anton},
  title = {A reinforcement learning method for maximizing undiscounted rewards},
  booktitle = {Proceedings of the tenth international conference on machine learning},
  year = {1993},
  volume = {298},
  pages = {298--305},
  owner = {Kurt},
  timestamp = {2015.02.18}
}

@INPROCEEDINGS{Sebag1997,
  author = {Mich{\`e}le Sebag and C{\'e}line Rouveirol},
  title = {Tractable Induction and Classification in First Order Logic Via Stochastic
	Matching},
  booktitle = {IJCAI},
  year = {1997},
  pages = {888-893},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@ARTICLE{DBLP:journals/jmlr/SegalPRKF05,
  author = {Eran Segal and Dana Pe'er and Aviv Regev and Daphne Koller and Nir
	Friedman},
  title = {Learning Module Networks},
  journal = {Journal of Machine Learning Research},
  year = {2005},
  volume = {6},
  pages = {557-588},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://www.jmlr.org/papers/v6/segal05a.html},
  review = {hidden variable model: assigns nodes to clusters (modules) s.t. each
	node in the cluster has the same parents and the same CP-table. Uses
	regression trees for CP tables. (Really, one node for each parent).
	Uses a kind of EM style algorithm for learning the clusters. Mentions
	multiple hypothesis testing.}
}

@INPROCEEDINGS{Segal2004,
  author = {Richard Segal and Jason Crawford and Jeffrey Kephart and Barry Leiba},
  title = {SpamGuru: An Enterprise Anti-Spam Filtering System},
  booktitle = {Proceedings of the First Conference on Email and Anti-Spam (CEAS)},
  year = {2004},
  note = {Available: \url{http://www.ceas.cc/papers-2004/126.pdf}},
  keywords = {GENERAL},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.ceas.cc/papers-2004/126.pdf}
}

@ARTICLE{Seigneur2004,
  author = {{Jean-Marc} Seigneur and {Christian Damsgaard} Jensen},
  title = {Privacy Recovery with Disposable Email Addresses},
  journal = {IEEE Security and Privacy},
  year = {2004},
  volume = {1},
  pages = {35--39},
  number = {6},
  note = {Available: \url{http://www.computer.org/security/v1n6/j6sei.htm}},
  keywords = {ADDRESS},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.computer.org/security/v1n6/j6sei.htm}
}

@BOOK{bib:sen,
  title = {Choice, Welfare and Measurement},
  publisher = {Blackwell},
  year = {1982},
  author = {A.K. Sen},
  address = {Oxford},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Sen2008,
  author = {Prithviraj Sen and Galileo Namata and Mustafa Bilgic and Lise Getoor
	and Brian Gallagher and Tina Eliassi-Rad},
  title = {Collective Classification in Network Data},
  journal = {AI Magazine},
  year = {2008},
  volume = {29},
  pages = {93-106},
  number = {3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2157},
  file = {Sen2008.pdf:Sen2008.pdf:PDF},
  keywords = {statistical-relational},
  owner = {oliverschulte},
  review = {collective classification survey},
  timestamp = {2011.05.03}
}

@INPROCEEDINGS{Sergeant2003,
  author = {Matt Sergeant},
  title = {Internet Level Spam Detection and SpamAssassin 2.50},
  booktitle = {Proceedings of the Spam Conference},
  year = {2003},
  note = {Available: \url{http://axkit.org/docs/presentations/spam/SpamConf2003.ppt}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://axkit.org/docs/presentations/spam/SpamConf2003.ppt}
}

@INPROCEEDINGS{Shachter1998,
  author = {R. D. Shachter},
  title = {{B}ayes-Ball: The Rational Pastime (for Determining Irrelevance and
	Requisite Information in Belief Networks and Influence Diagrams)},
  booktitle = {Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
	Conference},
  year = {1998},
  pages = {480-487},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Shafiei2010,
  author = {Shafiei, Mahdi and Chipman, Hugh},
  title = {Mixed-Membership Stochastic Block-Models for Transactional Networks},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Data Mining},
  year = {2010},
  series = {ICDM '10},
  pages = {1019--1024},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  acmid = {1934629},
  doi = {http://dx.doi.org/10.1109/ICDM.2010.88},
  isbn = {978-0-7695-4256-0},
  keywords = {Social Network Analysis, Clustering, Mixedmembership, Variational
	EM, Email Data},
  numpages = {6},
  url = {http://dx.doi.org/10.1109/ICDM.2010.88}
}

@ARTICLE{Shalizi2013,
  author = {Shalizi, Cosma Rohilla and Rinaldo, Alessandro},
  title = {Consistency under sampling of exponential random graph models},
  journal = {Annals of statistics},
  year = {2013},
  volume = {41},
  pages = {508},
  number = {2},
  file = {Shalizi2013.pdf:Shalizi2013.pdf:PDF},
  keywords = {statistical-relational},
  publisher = {NIH Public Access}
}

@ARTICLE{Shalizi2011,
  author = {Shalizi, Cosma Rohilla and Thomas, Andrew C},
  title = {Homophily and contagion are generically confounded in observational
	social network studies},
  journal = {Sociological Methods \& Research},
  year = {2011},
  volume = {40},
  pages = {211--239},
  number = {2},
  file = {Shalizi2011.pdf:Shalizi2011.pdf:PDF},
  keywords = {statistical-relational},
  publisher = {Sage Publications}
}

@ARTICLE{Shannon,
  author = {Claude E. Shannon},
  title = {A Mathematical Theory of Communication},
  journal = {Bell System Technical Journal},
  year = {1948},
  volume = {27},
  number = {379-423}
}

@INPROCEEDINGS{Sharma1997,
  author = {Arun Sharma and Frank Stephan and Yuri Ventsov},
  title = {Generalized Notions of Mind Change Complexity},
  booktitle = {Computational Learing Theory},
  year = {1997},
  pages = {96-108},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Shashua05,
  author = {Amnon Shashua and Tamir Hazan},
  title = {Non-negative tensor factorization with applications to statistics
	and computer vision},
  booktitle = {Proceedings of the 22nd international conference on Machine learning},
  year = {2005},
  pages = {792--799},
  address = {Bonn, Germany},
  publisher = {{ACM}},
  abstract = {We derive algorithms for finding a non-negative n-dimensional tensor
	factorization {(n-NTF)} which includes the non-negative matrix factorization
	{(NMF)} as a particular case when n = 2. We motivate the use of {n-NTF}
	in three areas of data analysis: (i) connection to latent class models
	in statistics, (ii) sparse image coding in computer vision, and (iii)
	model selection problems. We derive a "direct" positive-preserving
	gradient descent algorithm and an alternating scheme based on repeated
	multiple rank-1 problems.},
  doi = {10.1145/1102351.1102451},
  isbn = {1-59593-180-5},
  keywords = {statistical-relational},
  owner = {oliverschulte},
  timestamp = {2008.12.30},
  url = {http://portal.acm.org/citation.cfm?id=1102451}
}

@INPROCEEDINGS{wangMondial,
  author = {Rong She and Ke Wang and Yabo Xu and Philip S. Yu},
  title = {Pushing Feature Selection ahead of Join},
  booktitle = {SIAM SDM},
  year = {2005},
  pages = {536-540},
  owner = {bba18},
  timestamp = {2010.10.13}
}

@ARTICLE{Shinohara1991a,
  author = {A. Shinohara and S. Miyano},
  title = {Teachability in computational learning},
  journal = {New Generation Computing},
  year = {1991},
  volume = {8},
  number = {4},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Shinohara1991,
  author = {T. Shinohara},
  title = {Inductive inference of monotonic formal systems from positive data},
  journal = {New Gen. Comput.},
  year = {1991},
  volume = {8},
  pages = {371--384},
  number = {4},
  issn = {0288-3635},
  owner = {hkhosrav},
  publisher = {Ohmsha},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Shinohara1995,
  author = {Takeshi Shinohara and Setsuo Arikawa},
  title = {Pattern Inference},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report},
  year = {1995},
  pages = {259--291},
  publisher = {Springer-Verlag},
  isbn = {3-540-60217-8},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Sidhu2014,
  author = {Sidhu, Gagan and Caffo, Brian},
  title = {MONEYBaRL: Exploiting pitcher decision-making using Reinforcement
	Learning},
  journal = {The Annals of Applied Statistics},
  year = {2014},
  volume = {8},
  pages = {926--955},
  number = {2},
  month = {06},
  doi = {10.1214/13-AOAS712},
  file = {:Sidhu2014.pdf:PDF},
  owner = {Kurt},
  publisher = {The Institute of Mathematical Statistics},
  timestamp = {2014.11.07},
  url = {http://dx.doi.org/10.1214/13-AOAS712}
}

@INPROCEEDINGS{Siefkes2004,
  author = {Christian Siefkes and Fidelis Assis and Shalendra Chhabra and William
	S. Yerazunis},
  title = {Combining {Winnow} and Orthogonal Sparse Bigrams for Incremental
	Spam Filtering},
  booktitle = {Proceedings of the 8th European Conference on Principles and Practice
	of Knowledge Discovery in Databases (PKDD 2004)},
  year = {2004},
  editor = {Jean-Fran{\c c}ois Boulicaut and Floriana Esposito and Fosca Giannotti
	and Dino Pedreschi},
  volume = {3202},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {410--421},
  publisher = {Springer},
  note = {Available: \url{http://www.siefkes.net/papers/winnow-spam.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.siefkes.net/papers/winnow-spam.pdf}
}

@CONFERENCE{Sill2010,
  author = {Joseph Sill},
  title = {Improved NBA Adjusted +/- Using Regularization and Out-of-Sample
	Testing},
  booktitle = {MIT sloan Sports Analytics Conference},
  year = {2010},
  timestamp = {2010.03.06}
}

@ARTICLE{Silva2006,
  author = {Ricardo Silva and Richard Scheines and Clark Glymour and Peter Spirtes},
  title = {Learning the Structure of Linear Latent Variable Models},
  journal = {Journal of Machine Learning Research},
  year = {2006},
  volume = {7},
  pages = {191-246},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://www.jmlr.org/papers/v7/silva06a.html},
  file = {link to glymour on hidden variables:/Users/oschulte/Library/texmf/Silva2006.pdf:PDF},
  keywords = {{B}ayes Nets, digging}
}

@INBOOK{PECOTA,
  pages = {5-10},
  title = {PECOTA 2004: A Look Back and a Look Ahead},
  publisher = {New York: Workman Publishers},
  year = {2004},
  author = {Nate Silver},
  booktitle = {Baseball Prospectus 2004}
}

@BOOK{Silvey1975,
  title = {Statistical inference},
  publisher = {London : Chapman and Hall ; New York : Wiley},
  year = {1975},
  author = {S. D. Silvey},
  series = {Monographs on statistical subjects},
  edition = {Reprinted with corrections},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Singh2008,
  author = {Singh, Ajit P. and Gordon, Geoffrey J.},
  title = {Relational learning via collective matrix factorization},
  booktitle = {Proceedings of the 14th ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2008},
  series = {KDD '08},
  pages = {650--658},
  address = {New York, NY, USA},
  publisher = {ACM}
}

@ARTICLE{Singh1995,
  author = {M. Singh and M. Valtorta},
  title = {Construction of {B}ayesian network structures from data - A brief
	survey and an efficient algorithm},
  journal = {International Journal of Approximate Reasoning},
  year = {1995},
  volume = {12},
  pages = {111-131},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Graepel_CIKM13,
  author = {Sameer Singh and Thore Graepel},
  title = {Automated Probabilistic Modelling for Relational Data},
  year = {2013},
  comment = {preliminary version is Graepel_NIP12_workshop},
  file = {:Graepel_CIKM13.pdf:PDF},
  owner = {zqian},
  timestamp = {2013.09.20}
}

@ARTICLE{Graepel_NIP12_workshop,
  author = {Sameer Singh and Thore Graepel},
  title = {Compiling Relational Database Schemata into Probabilistic Graphical
	Models},
  journal = {CoRR},
  year = {2012},
  volume = {abs/1212.0967},
  comment = {NIPS 2012 Workshop on Probabilistic Programming},
  file = {:Graepel_NIP12_workshop.pdf:PDF},
  owner = {zqian},
  timestamp = {2013.09.20}
}

@ARTICLE{ParagSingla2008,
  author = {Parag Singla and Pedro Domingos},
  title = {Discriminative structure and parameter learning for Markov logic
	networks},
  year = {2008},
  pages = {416--423},
  address = {New York, NY, USA},
  booktitle = {ICML '08: Proceedings of the 25th international conference on Machine
	learning},
  doi = {http://doi.acm.org/10.1145/1390156.1390209},
  file = {:D\:\\Hassan\\Subversion related\\references\\Discriminative training of MLN.pdf:PDF},
  isbn = {978-1-60558-205-4},
  location = {Helsinki, Finland},
  owner = {hkhosrav},
  publisher = {ACM},
  timestamp = {2009.05.11}
}

@ARTICLE{bib:sklar,
  author = {Lawrence Sklar},
  title = {Methodological Conservatism},
  journal = {Philosophical Review},
  year = {1975},
  volume = {LXXXIV},
  pages = {374-400},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@BOOK{Smullyan1961,
  title = {Theory of Formal Systems},
  publisher = {Princeton University Press},
  year = {1961},
  author = {Raymond M. Smullyan},
  volume = {47},
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  address = {Princeton, New Jersey},
  language = {USenglish},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@MASTERSTHESIS{Spagnola2013,
  author = {Spagnola, Nathan},
  title = {The Complete Plus-Minus: A Case Study of The Columbus Blue Jackets},
  school = {University of South Carolina},
  year = {2013},
  file = {:Spagnola2013.pdf:PDF},
  owner = {Kurt},
  timestamp = {2014.11.07},
  url = {retrieved from \url{http://scholarcommons.sc.edu/etd/2708}}
}

@BOOK{Spirtes2000,
  title = {Causation, Prediction, and Search},
  publisher = {MIT Press},
  year = {2000},
  author = {P. Spirtes and C. Glymour and R. Scheines},
  keywords = {digging},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Spirtes1995,
  author = {P. Spirtes and C. Meek},
  title = {Learning {{B}ayesian} network with discrete variables from data},
  booktitle = {KDD},
  year = {1995},
  pages = {294--299},
  file = {Spirtes1995.pdf:Spirtes1995.pdf:PDF},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INCOLLECTION{spirtes95:_learn_bayes,
  author = {P. Spirtes and C. Meek},
  title = {Learning {{B}ayesian} networks with discrete variables from data},
  booktitle = {Proceedings of First International Conference on Knowledge Discovery
	and Data Mining, {\rm Montreal, QU}},
  publisher = {Morgan Kaufmann},
  year = {1995},
  pages = {294-299},
  month = {August},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Spohn2005,
  author = {Wolfgang Spohn},
  title = {Enumerative Induction and Lawlikeness},
  journal = {Philosophy of Science},
  year = {2005},
  volume = {1},
  pages = {164-187},
  file = {Spohn2005.pdf:Spohn2005.pdf:PDF},
  owner = {oschulte},
  timestamp = {2009.10.21}
}

@INCOLLECTION{bib:spohn,
  author = {W. Spohn},
  title = {Ordinal Conditional Functions: A dynamic Theory of Epistemic States},
  booktitle = {Causation, Decision, Belief Change, and Statistics},
  publisher = {Reidel},
  year = {1987},
  volume = {2},
  pages = {105-134},
  address = {Dordrecht},
  editors = {W. L. Harper and B. Skyrms},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Srinivas1993,
  author = {Sampath Srinivas},
  title = {A Generalization of the Noisy-Or Model},
  booktitle = {Proceedings of the 9th Annual Conference on Uncertainty in Artificial
	Intelligence (UAI-93)},
  year = {1993},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann},
  owner = {hkhosrav},
  timestamp = {2009.08.26}
}

@ARTICLE{Srinivasan1996,
  author = {Srinivasan, A. and Muggleton, SH and Sternberg, MJE and King, RD},
  title = {{Theories for mutagenicity: A study in first-order and feature-based
	induction}},
  journal = {Artificial Intelligence},
  year = {1996},
  volume = {85},
  pages = {277--299},
  number = {1-2},
  file = {Srinivasan1996.pdf:Srinivasan1996.pdf:PDF},
  keywords = {ilp, statistical-relational},
  owner = {oschulte},
  publisher = {Elsevier},
  timestamp = {2010.01.19}
}

@ARTICLE{Stair2011,
  author = {Stair, Anthony and Neral, John and Thomas, Logan and Mizak, Daniel},
  title = {Team Performance Characteristics Which Influence Wins in the National
	Hockey League.},
  journal = {Journal of International Business Disciplines},
  year = {2011},
  volume = {6},
  pages = {47 - 56},
  number = {2},
  abstract = {This paper examines the influence of team performance characteristics
	on wins in the National Hockey League using a multiplicative model.
	Six of the thirteen independent variables are significant at the
	99% level: power play percentage, whether a team scores first, save
	percentage, blocked shots, shootout shooting percentage, and faceoff
	percentage. Three of the thirteen independent variables were significant
	at the 95% level: major penalties, hits, and total shots. Variables
	that were not significant were: penalty minutes, shootout save percentage,
	giveaways and takeaways.},
  file = {:Stair2011_Team Performance Characteristics Which Influence Wins In The National Hockey League.pdf:PDF},
  keywords = {HOCKEY teams, HOCKEY techniques, HOCKEY -- Goalkeeping, POWER play
	(Sports), HOCKEY -- Defense, NATIONAL Hockey League},
  owner = {Kurt},
  timestamp = {2013.12.20},
  url = {http://proxy.lib.sfu.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=70498089&site=ehost-live}
}

@ARTICLE{bib:stal-revise,
  author = {Robert Stalnaker},
  title = {Knowledge, Belief and Counterfactual Reasoning in Games},
  journal = {Economics and Philosophy},
  year = {1996},
  volume = {12},
  pages = {133-163},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{bib:stalnaker-solutions,
  author = {Robert Stalnaker},
  title = {On the Evaluation of Solution Concepts},
  journal = {Theory and Decision},
  year = {1996},
  volume = {37},
  pages = {49-73},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INCOLLECTION{bib:stal-cond,
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  publisher = {Reidel},
  year = {1981},
  address = {Dordrecht},
  editors = {W. Harper, R. Stalnaker, and G. Pearce},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@MASTERSTHESIS{Stone2003,
  author = {Trevor Stone},
  title = {Parameterization of Nave {B}ayes for Spam Filters},
  school = {University of Colorado at Boulder},
  year = {2003},
  type = {Masters Comphrehensive Exam},
  note = {Available: \url{http://trevorstone.org/school/spamfiltering.pdf}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://trevorstone.org/school/spamfiltering.pdf}
}

@BOOK{bib:linear,
  title = {Introduction to Linear Algebra},
  publisher = {Wellesley-Cambridge Press},
  year = {1993},
  author = {Gilbert Strang},
  address = {Wellesley, Mass.},
  keywords = {linear algebra},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{ILP-knowledge,
  author = {Struyf, Jan and Ramon, Jan and Bruynooghe, Maurice and Verbaeten,
	Sofie and Blockeel, Hendrik},
  title = {Compact Representation of Knowledge Bases in Inductive Logic Programming},
  journal = {Mach. Learn.},
  year = {2004},
  volume = {57},
  pages = {305--333},
  number = {3},
  address = {Hingham, MA, USA},
  doi = {http://dx.doi.org/10.1023/B:MACH.0000039780.84966.04},
  issn = {0885-6125},
  publisher = {Kluwer Academic Publishers}
}

@INPROCEEDINGS{Studeny1992,
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  title = {Conditional independence relations have no finite complete characterization},
  booktitle = {Information Theory, Statistical Decision Functions and Random Processes.
	Transactions of the 11th Prague Conference},
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  pages = {377--396},
  publisher = {Kluwer, Dordrecht - Boston - London (also Academia, Prague)},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@BOOK{Studeny2005,
  title = {Probabilistic Conditional Independence Structures},
  publisher = {Springer},
  year = {2005},
  author = {M. Studeny},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@INPROCEEDINGS{Sullivan2004,
  author = {Terry Sullivan},
  title = {The more things change: Volatility and stability in spam features},
  booktitle = {Proceedings of the Spam Conference},
  year = {2004},
  note = {Available: \url{http://www.qaqd.com/research/mit04sum.html}},
  keywords = {BAYESIAN},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://www.qaqd.com/research/mit04sum.html}
}

@BOOK{Sun2012,
  title = {Mining Heterogeneous Information Networks: Principles and Methodologies},
  publisher = {Morgan \& Claypool Publishers},
  year = {2012},
  author = {Sun, Yizhou and Han, Jiawei},
  volume = {3},
  number = {2},
  pages = {1--159},
  journal = {Synthesis Lectures on Data Mining and Knowledge Discovery},
  owner = {zqian},
  timestamp = {2013.10.19}
}

@BOOK{Sutton1998,
  title = {Introduction to reinforcement learning},
  publisher = {MIT Press Cambridge},
  year = {1998},
  author = {Sutton, Richard S and Barto, Andrew G},
  volume = {135}
}

@book{sutton2018reinforcement,
  title={Reinforcement learning: An introduction},
  author={Sutton, Richard S and Barto, Andrew G},
  year={2018},
  publisher={MIT press}
}

@INPROCEEDINGS{Suzuki1993,
  author = {J Suzuki},
  title = {A Construction of {B}ayesian Networks from Databases Based on an
	MDL Principle},
  booktitle = {UAI-93},
  year = {1993},
  pages = {266-273},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Swartz2013,
  author = {Swartz, T.B. and Arce, A. and Parameswaran, M},
  title = {Assessing Value of the Draft Positions in Major League Soccer's SuperDraft},
  journal = {The Sport Journal},
  year = {2013},
  volume = {16},
  pages = {1-18},
  file = {:Swartz2013_Assessing Value of the Draft Positions in Major League Soccers SuperDraft.pdf:PDF},
  keywords = {Soccer, Draft, Time Played, Value},
  owner = {Kurt},
  timestamp = {2013.12.02}
}

@ARTICLE{swartz2014,
  author = {Swartz, Tim B and Arce, Adriano},
  title = {New insights involving the home team advantage},
  journal = {International Journal of Sports Science \& Coaching},
  year = {2014},
  volume = {9},
  pages = {681--692},
  number = {4},
  publisher = {SAGE Publications Sage UK: London, England}
}

@INCOLLECTION{bib:properties,
  author = {Chris Swoyer},
  title = {Properties},
  booktitle = {The Stanford Encyclopedia of Philosophy},
  year = {Winter 2000},
  editor = {Edward N. Zalta},
  owner = {oliverschulte},
  timestamp = {2008.12.30},
  url = {http://plato.stanford.edu/archives/win2000/entries/properties/}
}

@INCOLLECTION{Szajowski2014,
  author = {Szajowski, Krzysztof},
  title = {Stochastic Games and Learning},
  booktitle = {Encyclopedia of Systems and Control},
  publisher = {Springer London},
  year = {2014},
  editor = {Baillieul, John and Samad, Tariq},
  pages = {1-9},
  doi = {10.1007/978-1-4471-5102-9_33-1},
  keywords = {Markov decision process; Repeated game; Equilibrium; Dynamic programming;
	Reinforcement learning; Asynchronous dynamic programming; Q-learning},
  language = {English},
  owner = {Kurt},
  timestamp = {2014.11.07},
  url = {http://dx.doi.org/10.1007/978-1-4471-5102-9_33-1}
}

@ARTICLE{takasugi84:_can_dirac,
  author = {Takasugi, E.},
  title = {Can the neutrinoless double beta decay take place in the case of
	Dirac neutrinos?},
  journal = {Physics Letters B},
  year = {1984},
  volume = {149},
  pages = {327-376},
  number = {4-5},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INPROCEEDINGS{Taskar2002,
  author = {Benjamin Taskar and Pieter Abbeel and Daphne Koller},
  title = {Discriminative Probabilistic Models for Relational Data},
  booktitle = {UAI},
  year = {2002},
  pages = {485-492},
  publisher = {Morgan Kaufmann Publishers Inc.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  file = {Taskar2002.pdf:Taskar2002.pdf:PDF}
}

@MISC{Taskar2004,
  author = {Taskar, B. and Wong, M. and Abbeel, P. and Koller, D. },
  title = {Link prediction in relational data},
  year = {2004},
  abstract = {Many real-world domains are relational in nature, consisting of a
	set of objects related to each other in complex ways. This paper
	focuses on predicting the existence and the type of links between
	entities in such domains. We apply the relational Markov network
	framework of Taskar et al. to define a joint probabilistic model
	over the entire link graph --- entity attributes and links. The application
	of the RMN algorithm to this task requires the definition of probabilistic
	patterns over...},
  citeulike-article-id = {975344},
  keywords = {markov, network, prm, probabilistic},
  owner = {hkhosrav},
  posted-at = {2006-12-07 19:47:19},
  priority = {2},
  timestamp = {2009.08.26},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.3546}
}

@ELECTRONIC{Tegen2015,
  author = {Tegen},
  month = {January},
  year = {2015},
  title = {The best predictor for future performance is expected goals},
  url = {http://11tegen11.net/2015/01/05/the-best-predictor-for-future-performance-is-expected-goals/},
  owner = {oschulte},
  timestamp = {2016.03.04}
}

@ARTICLE{bib:tennant,
  author = {Neil Tennant},
  title = {Changing the Theory of Theory Change: Reply to My Critics},
  journal = {The British Journal for the Philosophy of Science},
  year = {1997},
  volume = {48},
  pages = {569--586},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INCOLLECTION{bib:euge-comput,
  author = {Eugenia Ternovskaia},
  title = {Automata Theory for Reasoning about Actions},
  booktitle = {Proceedings of the Sixteenth International Joint Conference on Artificial
	Intelligence},
  publisher = {IJCAI},
  year = {1999},
  pages = {153--158},
  address = {Stockholm, Sweden},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@INCOLLECTION{bib:euge-models,
  author = {Eugenia Ternovskaia},
  title = {Inductive Definability and the Situation Calculus},
  booktitle = {Transactions and Change in Logic Databases},
  publisher = {LNCS},
  year = {1997},
  volume = {1472},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@MISC{2008a,
  author = {{The Tetrad Group}},
  title = {The {Tetrad} Project},
  year = {2008},
  note = {http://www.phil.cmu.edu/ projects/tetrad/},
  key = {CMU Tetrad Project},
  keywords = {digging},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{blog,
  author = {A.C. Thomas and Sam Ventura},
  title = {The Highway to WAR: Defining and Calculating the Components for Wins
	Above Replacement},
  year = {2015},
  note = {Available at \url{https://aphockey.files.wordpress.com/2015/04/sam-war-1.pdf}}
}

@ARTICLE{Thomas2013,
  author = {Thomas, A.C. and Ventura, S.L. and Jensen, S. and Ma, S.},
  title = {Competing Process Hazard Function Models for Player Ratings in Ice
	Hockey},
  journal = {The Annals of Applied Statistics},
  year = {2013},
  volume = {7},
  pages = {1497-1524},
  number = {3},
  abstract = {Evaluating the overall ability of players in the National Hockey League
	(NHL) is a difficult task. Existing methods such as the famous ?plus/minus?
	statistic have many shortcomings. Standard linear regression methods
	work well when player substitutions are relatively uncommon and scoring
	events are relatively common, such as in basketball, but as neither
	of these conditions exists for hockey, we use an approach that embraces
	the unique characteristics of the sport. We model the scoring rate
	for each team as its own semi-Markov process, with hazard functions
	for each process that depend on the players on the ice. This method
	yields offensive and defensive player ability ratings which take
	into account quality of teammates and opponents, the game situation,
	and other desired factors, that themselves have a meaningful interpretation
	in terms of game outcomes. Additionally, since the number of parameters
	in this model can be quite large, we make use of two different shrinkage
	methods depending on the question of interest: full Bayesian hierarchical
	models that partially pool parameters according to player position,
	and penalized maximum likelihood estimation to select a smaller number
	of parameters that stand out as being substantially different from
	average. We apply the model to all five-on-five (full-strength) situations
	for games in five NHL seasons.},
  doi = {10.1214/13-AOAS646},
  file = {:Thomas2013_Competing Process Hazard Function Models for Player Ratings in Ice Hockey.pdf:PDF},
  keywords = {Competing process models; Cox model; ice hockey; Bayesian inference;
	hierarchical models; player ratings},
  owner = {Kurt},
  timestamp = {2013.12.03}
}

@ARTICLE{Thomas2006,
  author = {Andrew C. Thomas},
  title = {The Impact of Puck Possession and Location on Ice Hockey Strategy},
  journal = {Journal of Quantitative Analysis in Sports},
  year = {2006},
  volume = {2},
  number = {1}
}

@ARTICLE{Thompson2006,
  author = {Thompson, S.K.},
  title = {Adaptive web sampling},
  journal = {Biometrics},
  year = {2006},
  volume = {62},
  pages = {1224--1234},
  number = {4},
  publisher = {Wiley Online Library}
}

@ARTICLE{Tingling,
  author = {P. Tingling and K. Masri and M. Martell},
  title = {Does Order Matter? An Empirical Analysis of NHL Draft Decisions},
  journal = {Sport, Business and Management: an International Journal},
  year = {2011},
  pages = {155-171},
  number = {2}
}

@BOOK{bib:modern-physics,
  title = {Modern Physics},
  publisher = {W. H. Freeman},
  year = {2002},
  author = {Paul Tipler and Ralph Llewellyn},
  edition = {4},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Tompkins2003,
  author = {T. Tompkins and D. Handley},
  title = {Giving e-Mail Back to the Users: Using Digital Signatures to Solve
	the Spam Problem},
  journal = {First Monday},
  year = {2003},
  volume = {8},
  number = {9},
  month = {September},
  note = {Available: \url{http://firstmonday.org/issues/issue8_9/tompkins/index.html}},
  keywords = {SIGNATURE},
  owner = {hkhosrav},
  timestamp = {2008.10.08},
  url = {http://firstmonday.org/issues/issue8_9/tompkins/index.html}
}

@ARTICLE{tong2001support,
  author = {Tong, Simon and Koller, Daphne},
  title = {Support vector machine active learning with applications to text
	classification},
  journal = {Journal of machine learning research},
  year = {2001},
  volume = {2},
  pages = {45--66},
  number = {Nov}
}

@INCOLLECTION{Torrey2009,
  author = {Lisa Torrey and Jude Shavlik},
  title = {Transfer Learning},
  booktitle = {Handbook of Research on Machine Learning Applications},
  publisher = {IGI Global},
  year = {2009},
  pages = {1--22},
  owner = {oschulte},
  timestamp = {2018.05.31}
}

@INPROCEEDINGS{Tsamardinos2003a,
  author = {I. Tsamardinos and C.F. Aliferis and A. Statnikov},
  title = {Algorithms for Large Scale Markov Blanket Discovery},
  booktitle = {The 16th International FLAIRS Conference},
  year = {2003},
  pages = {376-381},
  address = {St. Augustine, Florida},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@TECHREPORT{Tsamardinos2003,
  author = {I. Tsamardinos and C. F. Aliferis and A. Statnikov and L. E. Brown},
  title = {Scaling-Up {B}ayesian Network Learning to Thousands of Variables
	Using Local Learning Techniques},
  institution = {Vanderbilt University},
  year = {2003},
  number = {DSL TR-03-02},
  owner = {hkhosrav},
  timestamp = {2008.10.08}
}

@ARTICLE{Tsamardinos2006,
  author = {I. Tsamardinos and L. E. Brown and C. F. Aliferis},
  title = {The max-min hill-climbing {B}ayesian network structure learning algorithm},
  journal = {Machine Learning},
  year = {2006},
  volume = {65(1)},
  pages = {31???78},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
}

@ARTICLE{Tsamardinos2006a,
  author = {I. Tsamardinos and L. E. Brown and C. F. Aliferis},
  title = {The max-min hill-climbing {B}ayesian network structure learning algorithm},
  journal = {Machine Learning},
  year = {2006},
  volume = {65(1)},
  pages = {31?78},
  owner = {oliverschulte},
  timestamp = {2008.12.30}
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  keywords = {Graphical models, Query optimization, Selectivity estimation},
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repository: sfu-cl-lab/our-papers

repo_name: sfu-cl-lab/our-papers