CSE 645: Seminar in Languages

Course Outline:

We will read papers and discuss research on knowledge representation and programming in the presence of uncertainty.

Instructor:

C. R. Ramakrishnan

Schedule:

Fridays, 10:30 -- 12 noon, Room 1441

Papers:

• D. Heckerman, C. Meek, and D. Koller. Probabilistic Models for Relational Data.Technical Report MSR-TR-2004-30, Microsoft Research, March, 2004. ftp://ftp.research.microsoft.com/pub/tr/TR-2004-30.pdf
• Jennifer Neville, Matthew Rattigan, and David Jensen, Statistical relational learning: Four claims and a survey, http://kdl.cs.umass.edu/srl2003_upload/files/neville-paper.pdf
• Kristian Kersting, Luc De Raedt: Bayesian Logic Programs CoRR cs.AI/0111058: (2001) http://arxiv.org/abs/cs.AI/0111058
• L. De Raedt, K. Kersting, A. Kimmig, K. Revoredo, H. Toivonen. Compressing Probabilistic Prolog Programs, Machine Learning, 70,(2-3):151-168, 2008.
• A. Puech and S. Muggleton, A comparison of Stochastic logic programs and Bayesian logic programs, IJCAI03 workshop on learning statistical models from relational data.
• P. Domingos and M. Richardson, Markov Logic: A Unifying Framework for Statistical Relational Learning ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields.
• IBAL and ProPL:
  1. Avi Pfeffer, IBAL: A Probabilistic Rational Programming Language (IJCAI, 2001)
  2. Avi Pfeffer, Functional Specification of Probabilistic Process Models (AAAI, 2005)
• T. Sato, A glimpse of symbolic-statistical modeling by PRISM. Journal of Intelligent Information Systems, Vol.31, No.2, pp.161–176, 2008.
• Norman Ramsey and Avi Pfeffer Stochastic Lambda Calculus and Monads of Probability Distributions (POPL, 2002)
• B. Milch, B. Marthi, S. J. Russell, D. Sontag, D. L. Ong, and A. Kolobov, BLOG: probabilistic models with unknown objects (IJCAI, 2005).