Difference between revisions of "MLReadingGroup"
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** Nostalgia: Rifkin and Klautau. [http://five-percent-nation.mit.edu/PersonalPages/rif/Pubs/ovadefense.ps "In Defense of One-Vs-All Classification."] Journal of Machine Learning Research, Volume 5, pp. 101-141, 2004. | ** Nostalgia: Rifkin and Klautau. [http://five-percent-nation.mit.edu/PersonalPages/rif/Pubs/ovadefense.ps "In Defense of One-Vs-All Classification."] Journal of Machine Learning Research, Volume 5, pp. 101-141, 2004. | ||
* 1 December 2006 | * 1 December 2006 | ||
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** Leader: Jonathan Chang | ** Leader: Jonathan Chang | ||
** Paper: [http://acl.ldc.upenn.edu/P/P06/P06-1101.pdf Snow, Jurafsky, and Ng. "Semantic Taxonomy Induction from Heterogenous Evidence." Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 801-808, July 2006.] | ** Paper: [http://acl.ldc.upenn.edu/P/P06/P06-1101.pdf Snow, Jurafsky, and Ng. "Semantic Taxonomy Induction from Heterogenous Evidence." Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 801-808, July 2006.] |
Revision as of 08:36, 27 November 2006
Machine Learning Reading Group
Welcome to the wiki of the machine learning reading group.
Contents
Mailing list
We maintain an announcement/discussion list for the reading group. You may sign up for the list here.
Schedule
Our weekly meetings are Fridays, 3pm to 5pm, in CS 402.
Scheduled readings:
- 5 October 2006 THURSDAY 4:30PM this week
- Leader: Zafer
- Paper: Horst, R., Thoai, N. V. 1999. DC Programming: Overview. J. Optim. Theory Appl.
- (If you need a review: Hindi, H. 2004. A Tutorial on Convex Optimization. American Control Conference.)
- (Application for the interested: Argyriou A. et al. 2006. A DC-Programming Algorithm for Kernel Selection. ICML.)
- And another application Ellis, S. and Nayakkankuppam, V. Phylogenetic Analysis Via DC Programming .
- 13 October 2006
- Leader: Miro
- Papers:
- Paper:
- 20 October 2006
- Leader: Florian
- Paper: N Meinshausen and P Bühlmann, High Dimensional Graphs and Variable Selection With the Lasso, Annals of Statistics 34(3), 1436-1462
- Background reading: D Heckerman, DM Chickering, C Meek, R Rounthwaite, C Kadie, Dependency Networks for Inference, Collaborative Filtering, and Data Visualization, JMLR, 1(Oct):49-75, 2000
- 27 October 2006
- Leader:
- Paper:
- 3 November 2006
- Leader:
- Paper:
- 10 November 2006
- "Leader": Jordan
- Paper: Understanding the Yarowsky Algorithm Abney, S. 2004. Understanding the Yarowsky Algorithm. Comput. Linguist. 30, 3 (Sep. 2004), 365-395.
- Background: The original paper Yarowsky, D. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting on Association For Computational Linguistics (Cambridge, Massachusetts, June 26 - 30, 1995). Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, 189-196.
- Background: A good overview of the problem area Ide, N. and Véronis, J. 1998. Introduction to the special issue on word sense disambiguation: the state of the art. Comput. Linguist. 24, 1 (Mar. 1998), 2-40.
- 17 November 2006
- Boss: Berk Kapicioglu
- Paper: Weighted One-Against-All
- Nostalgia: Rifkin and Klautau. "In Defense of One-Vs-All Classification." Journal of Machine Learning Research, Volume 5, pp. 101-141, 2004.
- 1 December 2006
- Leader: Jonathan Chang
- Paper: Snow, Jurafsky, and Ng. "Semantic Taxonomy Induction from Heterogenous Evidence." Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 801-808, July 2006.
- Some useful (perhaps) background on the technique: Snow, Jurafsky, and Ng. "Learning Syntactic Patterns for Automatic Hypernym Discovery."
- Background on what they're trying to do: Miller. "WordNet: A Lexical Database for English." Communications of the ACM, Volume 38, Issue 11 (November 1995), Pages 39-41.
Proposed Topics and Papers
Please add further topics, suggest papers for particular topics, etc. here.
- Lasso
- The original paper: Robert Tibshirani. Regression shrinkage and selection via the Lasso. J. R. Statist. Soc. B 58, 1995.
- The Lasso Page
- Generalization properties:
- Sparse approximation:
- David Donoho. For most large underdetermined systems of linear equations, the minimal l1-norm near-solution approximates the sparsest near-solution, Tech. Report, August 2004.
- David Donoho. For most large underdetermined systems of linear equations, the minimal l1-norm solution is also the sparsest solution, Tech. Report, September 2004.
- Model selection: P. Buhlmann and B. Yu. Boosting, Model Selection, Lasso and Nonnegative Garotte. Tech. Report, 2005.
- Relatives:
- RODEO
- Least angle regression
- Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net. J. R. Statist. Soc. B, 2005 + Addendum
- Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. Efficient L1 regularized logistic regression. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), 2006.
- Optimization
- Language Applications
- Music
- Smith, J. 2005. Music and machine learning are great. ISMIR 2006 Proceedings.
- I've put the link above to give an example of how to link to a paper. It will soon be replaced by a link or description of one or more real papers on machine learning and music. --RebeccaFiebrink 20:50, 28 September 2006 (EDT)
- Smith, J. 2005. Music and machine learning are great. ISMIR 2006 Proceedings.
- Markov Decision Processes (MDPs) and Reinforcement Learning
- State-space abstraction/aggregation in MDPs
- The E^3 algorithm (Kearns and Singh)
- Inverse reinforcement learning
- Active Learning
- Deep Neural Networks
- Factor Graphs
Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables.- Tutorial: Factor Graphs and the Sum-Product Algorithm, F.R. Kschischang, B. Frey, H-A Loelinger, IEEE Transactions on Information Theory, Vol 47, No 2, Feb 2001.
- Application: Physical Network Models, C-H Yeang, T Ideker, T Jaakkola, Journal of Computational Biology, Vol 11, No 2-3, 2004
- Cross-validation
- Kohavi, R. 1995. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI).
- "Do 10-fold CV instead of leave-one-out," at least when doing model selection
- Rivals, I., and L. Personnaz. 1999. "On cross-validation for model selection." Neural Computation 11 (4).
- "Do statistical tests instead of leave-one-out," at least when doing model selection
- Elisseeff, A., and M. Pontil. 2003. "Leave-one-out error and stability of learning algorithms with applications." Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, J. Suykens et al. Eds.
- Reasons about sufficient conditions for LOO-CV error to approach generalization error for fixed algorithms (including kernel methods). A more theoretical (and maybe less practical) paper.
- see also Evgeniou, T., M. Pontil, and A. Elisseeff. 2004. "Leave-one-out error, stability, and generalization of voting combinations of classifiers." Machine Learning 55:(1): 71-97. (I haven't read this but it's very related.)
- Kohavi, R. 1995. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI).
Participants
(Participants, please add your name to the list below.)
Faculty
- David Blei
- Rob Schapire
PostDocs
- Florian Markowetz, LSI
Students
- Zafer Barutcuoglu, CS
- Jordan Boyd-Graber, CS
- Joseph Calandrino, CS
- Jonathan Chang, EE
- Miroslav Dudik, CS
- Rebecca Fiebrink, CS
- Berk Kapicioglu, CS
- Umar Syed, CS