MLRG/summer09
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| May 12 <font color="#FF0000">(in LCR)</font> | | May 12 <font color="#FF0000">(in LCR)</font> | ||
| - | || | + | || Learning with auxiliary information: [http://www.stanford.edu/~hllee/icml07-selftaughtlearning.pdf Self-taught learning], [http://books.nips.cc/papers/files/nips21/NIPS2008_0098.pdf Translated Learning] |
| - | || | + | || Piyush |
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| May 19 | | May 19 | ||
Revision as of 16:45, 6 May 2009
Contents |
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
- Piyush Rai, PhD Student, School of Computing
- Jagadeesh J, PhD Student, School of Computing
- Ruihong Huang, PhD Student, School of Computing
- Amit Goyal, PhD Student, School of Computing
- Arvind Agarwal, 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
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)
