There are three parts to this course's requirements:
The course schedule is at the bottom of this page.
Relational Learning with Statistical Predicate Invention: Better Models for Hypertext.
Mark Craven and Sean Slattery.
Machine Learning, 43(1-2): 97-119, 2001.
Prolog for First-Order Bayesian Networks: A Meta-interpreter Approach.
Hendrik Blockeel. KDD Workshop on Multi-Relational Data Mining, 2003.
Probabilistic Models for Relational Data.
D. Heckerman, C. Meek, and D. Koller. Technical Report MSR-TR-2004-30, Microsoft Research, March, 2004.
Learning Probabilistic Relational Models
Lise Getoor, Nir Friedman, Daphne Koller and Avi Pfeffer
(IJCAI'99 paper by same name and authors)
The relational vector-space model and industry classification
Abraham Bernstein, Scott Clearwater, and Foster Provost
Discriminative probabilistic models for relational data
Ben Taskar, Pieter Abbeel and Daphne Koller
Relational Reinforcement Learning
Saso Dzeroski, Luc De Raedt and Hendrik Blockeel (ICML'98 and ILP'98)
Inductive policy selection for first-order MDPs
SungWook Yoon, Alan Fern and Robert Givan (UAI'02)
A Multi-relational
decision tree learning algorithm - implementation and experiments
Anna Atramentov, H. Leiva and Vasant Honavar
Speeding up multi-relational data mining
Anna Atramentov and Vasant Honavar
Categorizing unsupervised relational learning algorithms
Hannah Blau and Amy McGovern
Aggregation versus selection bias, and relational neural networks
Hendrik Blockeel and Maurice Bruynooghe
Feature extraction languages for propositionalized relational learning
Chad Cumby and Dan Roth
Individuals, relations and structures in probabilistic models
James Cussens
Operations for learning with graphical models
Wray Buntine
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
Vitor Santos Costa, David Page, Maleeha Qazi and James Cussens (UAI'03)
Learning Markov networks: Maximum bounded tree-width graphs (Symposium on Discrete Algorithms, 2001)
David Karger and Nathan Srebro
Ecosystem analysis using probabilistic relational modeling
Bruce D'Ambrosio, Eric Altendorf, and Jane Jorgensen
Dynamic probabilistic relational models (IJCAI'03)
Sumit Sanghai, Pedro Domingos and Daniel Weld
Research on statistical relational learning at the University of Washington
Pedro Domingos,
Yeuhi Abe, Corin Anderson, Anhai Doan, Dieter Fox, Alon Halevy, Geoff
Hulten, Henry Kautz, Tessa Lau, Lin Liao, Jayant Madhavan, Mausam,
Donald J. Patterson, Matthew Richardson, Sumit Sanghai, Daniel Weld and
Steve Wolfman
Relational learning for securities market regulation
Henry Goldberg
Social network relational vectors for anonymous identity matching
Shawndra Hill
Mining massive relational databases
Geoff Hulten, Pedro Domingos, and Yeuhi Abe
Representational power of probabilistic-logical models: From upgrading to downgrading
Kristian Kersting
Logical Markov decision programs
Kristian Kersting and Luc De Raedt
First-order probabilistic models for information extraction
Bhaskara Marthi, Brian Milch, and Stuart Russell
A Note on the unification of information extraction and data mining using conditional-probability, relational models
Andrew McCallum and David Jensen
The Variable precision rough set inductive logic programming model -- a statistical relational learning perspective
R. Milton, V. Maheswari and A. Siromoney
Statistical relational learning: Four claims and a survey
Jennifer Neville, Matthew Rattigan, and David Jensen
Parameter estimation for stochastic context-free graph grammars
Tim Oates, Fang Huang, and Shailesh Doshi
Aggregation and concept complexity in relational learning
Claudia Perlich and Foster Provost
Aggregation-based feature invention and relational concept classes
Claudia Perlich and Foster Provost
Relational learning problems and simple models
Foster Provost, Claudia Perlich and Sofus Macskassy
Learning probabilistic relational planning rules (Fourteenth International Conference on Automated Planning and Scheduling , 2004)
Hanna M. Pasula, Luke S. Zettlemoyer and Leslie Pack Kaelbling
Statistical relational learning for link prediction
Alexandrin Popescul and Lyle H. Ungar
A comparison of stochastic logic programs and Bayesian logic programs
Aymeric Puech and Stephen Muggleton
Principles of Learning Bayesian Logic Programs
Kristian Kersting and Luc De Raedt
Learning statistical models of time-varying relational data
Sumit Sanghai, Pedro Domingos and Daniel Weld
A new perspective of statistical modeling with PRISM
Taisuke Sato and Neng-Fu Zhou
Relational learning: A web-page classification viewpoint
Sean Slattery
Statistical modeling of graph and network data
Padhraic Smyth
Label and link prediction in relational data
Ben Taskar, Pieter Abbeel, Ming-Fai Wong, and Daphne Koller
Toward a high-performance system for symbolic and statistical modeling
Neng-Fa Zhou, Taisuke Sato, and Koiti Hasidad