Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos@cs.pitt.edu
office hours: Tuesday 2:30-4:00pm, Wednesday 11:00-12:00am

Basics of Matrix algebra

• Sam Roweis. Matrix identity
• Tom Minka. Matrix Algebra.
• Mike Brookes The Matrix Reference Manual

• Lecture notes from CS2750
• R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. Second edition. John Wiley and Sons, 2000.
• M. Jordan. Eponential family. In Graphical models. Chapter 7.
• M. Jordan. The Multivariate Gaussian. In Graphical models. Chapter 12.

• Lecture notes from CS2750
• Hastie, Tibshirani and Friedman. Elements of statistical learning. Sections: 3.4, 4.3.1, 5.6., 5.8.

Basics:

• Lecture notes for CS2750
• Hastie, Tibshirani and Friedman. Elements of statistical learning. Section 14.5

Applications:

• Link analysis.
  • Borodin et al. Finding Authorities and Hubs From Link Structures on the World Wide Web
  • Jon M. Kleinberg. Authoritative Sources in a Hyperlinked Environment, Journal of ACM. 1999.
  • Sergey Brin, Lawrence Page The Anatomy of a Large-Scale Hypertextual Web Search Engine
  • Ng. Matrices, Vector Spaces, and Information Retrieval, SIAM Review, 1999.

Bayesian Belief networks: Exact inference

Overview of inferences in BBNs.

Complexity Results

• Cooper, G.F., The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks, Artificial Intell., 42, pp. 393-405, 1990.
• Dagum, P. and Luby,M.(1993). Approximating probabilistic inference in bayesian belief networks is np-hard. Artificial Intelligence, 60:141--153.

Variable elimination

• Rina Dechter. Bucket Elimination: A Unifying Framework for Reasoning. Artificial Intelligence, 1999.

• C. Huang, A. Darwiche. Inference in Belief Networks: A Procedural Guide.
• M. Jordan. Graphical models. Chapter 16. (see CS 2750 web page from Spring 02)

Pearl's message passing

• Peter Lucas. Lecture notes

Bayesian Belief networks: Monte Carlo inference

General introduction to Monte Carlo methods:

• Rubinstein. Simulation and the Monte Carlo Method. 1981.
• David MacKay. Introduction to Monte Carlo methods.
• Andrieu et al. An introduction to MCMC for Machine Learning. Machine Learning, vol. 50, pp.5-43, 2003.

Importance sampling for BBNs:

• A brief summary of existing Importance Sampling Algorithms for Bayesian Networks
• Paul Dagum, Michael Luby. An Optimal Approximation Algorithm For Bayesian Inference. Artif Intelligence, 93:1--27, 1997.

• Jian Cheng, Marek J. Druzdzel.AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks. Journal of Artificial Intelligence Research, 2000.
• Luis E. Ortiz, Leslie Pack Kaelbling. Adaptive Importance Sampling for Estimation in Structured Domains. In Proceedings of the UAI 2000, pp. 446-454, 2000.

• Kevin P. Murphy, Yair Weiss, Michael I. Jordan. Loopy Belief Propagation for Approximate Inference: An Empirical Study., 1999.
• Jonathan S. Yedidia Generalized Belief Propagation. 2000.
• Jonathan S. Yedidia, William T. Freeman, Yair Weiss Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms, 2002.

• Robert J. McEliece, David JC MacKay, Jung-Fu Cheng. Turbo Decoding as an Instance of Pearl's Belief Propagation Algorithm

Basics:

• Lecture notes for CS2750: Lecture 14
• Wray Buntine. A Guide to the Literature on Learning Probabilistic Networks From Data. 1996.
• David Heckerman A Tutorial on Learning With Bayesian Networks 1996.
• Wray Buntine. Operations for Learning with Graphical Models JAIR, 1994.

EM

Basics:

• Lecture notes for CS2750: Lecture 15 and Lecture 16
• A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal statistical society, vol. 39, issue 1, pp. 1-28, 1977.
• Expectation-maximization in M. Jordan, C. Bishop. Introduction to graphical models.

Structural EM

• Nir Friedman. The Bayesian structural EM algorithm. In Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI). 1998
• Singh, Moninder. Learning Bayesian Networks from Incomplete Data. In Proceedings of the 14th National Conf. on Artificial Intelligence (AAAI'97), Providence, RI, July 27-31.

• Michael E. Tipping, Chris M. Bishop. Probabilistic Principal Component Analysis, 1999. (TR in 1997)
• Sam Roweis. EM Algorithms for PCA and SPCA. NIPS-1998.
• Tipping and Bishop 1998 Mixtures of Probabilistic Principal Component Analysers

Variational methods

Introduction:

• Jordan et al An Introduction to Variational Methods for Graphical Models, 1998.
• Tommi Jaakkola Tutorial on variational approximation methods
• David J.C. MacKay. Variational Methods

Variational ML learning for component analysis

• Z. Ghahramani. Factorial Learning and the EM Algorithm 1996.
• Z. Ghahramani and G. Hinton. The EM Algorithm for Mixtures of Factor Analyzers , 1996.
• A.P. Dunmur, D.M. Titterington (1997) On a Modification to the Mean Field EM Algorithm in Factorial Learning
• Ghahramani and Beal (2000) Graphic Models and Variational Methods

Variational Bayesian learning:

• Haigai Attias Inferring parameters and structure of latent variable models by variational Bayes (1999)
• Lawrence N.D. and Bishop C.M. Variational Bayesian Independent Component Analysis 1999
• Ghahramani and Beal Variational Inference for Bayesian Mixture of Factor Analyzers , 2000.
• Ghahramani and Beal (2000) Graphic Models and Variational Methods
• X. Lu, M. Hauskrecht, R.S. Day. Variational Bayesian learning of the cooperative vector quantizer model. Part I: The Theory. Technical Report, Center for Biomedical Informatics, CBMI-02-181, 2002.

Other variational learning papers

• Wim Wiegerinck Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
• Haft, Hofmann and Tresp 2002Model-Independent Mean Field Theory as a Local Method for Approximate Propagation of Information
• Matthew J. Beal and Z. Ghahramani The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical structures , 2003.

Latent Variable Models for text analysis, information retrieval and link analysis

Aspect model and PHITS (multinomial PCA):

• Thomas Hoffman. Probabilistic Latent Semantic Analysis. UAI-99, 1999.
• Thomas Hofmann. Probabilistic Latent Semantic Indexing. SIGIR-99, 1999.
• David Cohn and Huan Chang. Learning to probabilistically identify Authoritative documents
• David Cohn and Thomas Hoffman. The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
• David M. Blei, Andrew Y. Ng, Michael I. Jordan. Latent Dirichlet Allocation. NIPS-02.
• Wray Buntine and Sami Perttu. Is multinomial PCA multi-faceted clustering or dimensionality reduction AI in statistics, 2003.

Support vector machines (basics):

• Lecture notes from CS2750
• Christopher J.C. Burges A Tutorial on Support Vector Machines for Pattern Recognition
• Alex J. Smola, Bernhard Schölkopf. A Tutorial on Support Vector Regression.
• www.kernel-machines.org

Kernel methods (basics):

• Bernhard Scholkopf's Statistical Learning and Kernel Methods, 2000
• Bernhard Scholkopf's The Kernel Trick for Distances NIPS-2000.
• Bernhard Schölkopf, Isabelle Guyon, Jason Weston Statistical Learning and Kernel Methods in Bioinformatics
• B. Schokopf and A. Smola. Learning with kernels. MIT Press, 2002.

Kernel PCA:

• B. Schokopf, A. Smola, and KR. Muller. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 1998. (see Summary by Ian Fasel)

Kernel ICA:

• Francis R. Bach, Michael I. Jordan. Kernel Independent Component Analysis. 2001.

Various kernels

• String matching:
  • S. V. N. Vishwanathan, Alexander J. Smola. Fast Kernels for String and Tree Matching. NIPS-2002 (or a more recent paper )
• Graphs and Discrete structures:
  • Risi Imre Kondor John Lafferty. Diffusion Kernels on Graphs and Other Discrete Structures
  • John Lafferty, Guy Lebanon. Information Diffusion Kernels.