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	== Paper Summaries ==		== Paper Summaries ==
		+	=== Learning with auxiliary information / May 12 (Piyush) ===
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	==Past Semesters==		==Past Semesters==

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Revision as of 17:45, 12 May 2009

Contents 1 CS7941: Assorted Topics in Machine Learning 1.1 Participants 1.2 Schedule 1.3 Paper Summaries 1.3.1 Learning with auxiliary information / May 12 (Piyush) 1.4 Past Semesters

CS7941: Assorted Topics in Machine Learning

Time: Tuesdays, noon-1:30

Location: Graphics annex (MEB 3515), except as noted

Each week will include a discussion of 2-4 papers from a recent conference (past 24 months or so) on roughly the same topic. Your job as the presenter is to do a compare/contrast thing and teach us about some area. If you want to run your selections by me ahead of time, that's fine. If not, that's fine, too.

Please don't sign up for dates until you've chosen papers and please try to let the new students sign up first so they can get times/papers that they're most comfortable with.

Feel free to select papers from ML conferences (ICML, NIPS, UAI, etc) *or* applications conferences (ACL, CVPR, SIGIR, ISMB, etc.) but be sure that the focus in the latter case is on the machine learning, not on the application. Of course, if you'd like to choose a technical ML paper and then an applied paper and present them together, that might be even more fun!

Participants

• Hal Daumé III, Assistant Professor, School of Computing
• Arvind Agarwal, PhD Student, School of Computing
• Amit Goyal, PhD Student, School of Computing
• Ruihong Huang, PhD Student, School of Computing
• Jagadeesh J, PhD Student, School of Computing
• Piyush Rai, PhD Student, School of Computing
• Adam R. Teichert, MS Student, School of Computing
• Jiarong Jiang, PhD Student, School of Computing

Schedule

Date	Papers	Presenter
May 12 (in LCR)	Learning with auxiliary information: Self-taught learning, Translated Learning	Piyush
May 19	Deep Boltzmann Machines and Analyzing human feature learning as nonparametric Bayesian inference	Ruihong
May 26	TBD
Jun 9	TBD
Jun 23	TBD
Jun 30	TBD
Jul 7	TBD
Jul 21	TBD
Jul 28	TBD
Aug 11	TBD
Aug 18	TBD

Paper Summaries

Learning with auxiliary information / May 12 (Piyush)

Coming soon..

Past Semesters

• Spring 2009 (Learning Theory)
• Fall 2008 (Graphical Models)
• Summer 2008 (Multitask Learning)
• Spring 2008 (Kernel Methods)
• Fall 2007 (Unsupervised Bayes)
• Summer 2007 (Manifolds)