Reading Group on Statistical Learning, Fall 2003
Computer Science Department and Statistics Department, UC Irvine
Organizers: Padhraic Smyth (CS) and David Van Dyk (Statistics)
Logistics: Meet on alternate Tuesdays from 4 to 5pm in CS 432
Note that on the other Tuesdays we have the AI and
Statistics seminar series.
September 30th: Topic Models for Documents
Latent
Dirichlet Allocation
David M. Blei, Andrew Y. Ng, Michael I. Jordan, Journal of Machine
Learning Research, 3(Jan):993-1022, 2003.
Finding
Scientific Topics.
Griffiths, T., &
Steyvers, M. (submitted), Proceedings of the National Academy
of Sciences.
Discussant: Michal Rosen-Zvi (michal@ics.uci.edu)
October 14th: Conditional Random Fields
Efficiently
Inducing Features of Conditional Random Fields .
Andrew McCallum, Uncertainty in Artificial Intelligence Conference
(UAI), 2003.
Shallow
Parsing with Conditional Random Fields
F. Sha and F. Pereira, Proceedings of Human Language Technology,
NAACL, 2003
Discussant: David Van Dyk (dvd@ics.uci.edu)
October 28th: Non-Parametric Priors and Tree-Structured Priors
Defining
Priors for Distributions using Dirichlet Diffusion Trees.
Radford Neal, Technical Report, 2001.
Hierarchical Topic
Models and the Nested Chinese Restaurant Process.
Blei et al, NIPS 2003, to appear.
Discussant: Max Welling (welling@ics.uci.edu)
November 11th: "Information Bottleneck" algorithms
The Information Bottleneck
EM Algorithm.
Elidan and Friedman, Uncertainty in Artificial Intelligence Conference
(UAI), 2003.
Related Paper:
Maximum Likelihood
and the Information Bottleneck
Slonim and Weiss, NIPS 2002.
Discussant: Se Young Kim (sykim@ics.uci.edu)
BELOW ARE SUGGESTED TOPICS/PAPERS FOR THE FUTURE:
THESE ARE SUBJECT TO CHANGE DEPENDING ON THE GROUP'S INTERESTS.
November ?: SVMs and Kernel-based Learning
An Introduction to Kernel-Based Learning Algorithms
K. Muller et al., IEEE Transactions on Neural Networks, 2001
Max-Margin
Markov Networks
B. Taskar, C. Guestrin, and D. Koller, NIPS Conference, to appear, December
2003.
Discussant: TBD
November ?: Probabilistic Learning and Inference in Computer Vision
Bayesian Object Localisation in Images
O Sullivan, Blake, Isard, and McCormick, IJCCV, 2001.
Real-Valued Graphical Models for Computer Vision
Michael Isard, CVPR, 2003.
Discussant: TBD
Other Possible Topics (TBD) - for November 11th and 25th
- November 25th? the relation between probabilistic relational models
and hierarchical Bayesian models
- December 9th: several of us will be away at the NIPS meeting in
Vancouver that week
- December 16th? the intersection of computer graphics, statistical
sampling methods, and machine learning (Jim Arvo)