MLRG/spring10
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| - | || [http:// | + | || [http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf Maximum Entropy Markov Models] versus [http://www.cis.upenn.edu/~pereira/papers/crf.pdf Conditional Random Fields] |
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Revision as of 00:26, 12 January 2010
Contents |
CS7941: Topics in Machine Learning
Time: Thursdays, 10:45-12:05
Location: MEB 3105, except as noted
Topic: Structured Prediction
Expectations
Each week will consist of two related papers, and there will be two presenters. Each presenter is to (a) advocate their approach and (b) critique the other approach (kindly... especially if it's my paper!). The presentations will go roughly as follows:
- 20 mins for presenter A to talk about paper A
- 20 mins for presenter B to talk about paper B
- 10 mins for presenter A to critique paper B
- 10 mins for presenter B to critique paper A
- 20 mins open discussion trying to reach a resolution
The presenters should write short summaries ahead of time (by 11:59pm the day before the meeting) about their papers, posted at the bottom of this wiki.
You may take this course for 1 credit or 2 credits. If you take it for one credit, you only need to do what's above. If you take it for two credits, then you additionally need to help with the development of an implementation of Searn on top of John Langford's VW engine.
Participants
- Hal Daumé III, Assistant Professor, School of Computing
Schedule
Please do not sign up until the semester starts, except for day 1... we need to give "the youth" a chance to sign up before senior folks to!
| Date | Papers | Presenter |
|---|---|---|
| Introduction to Structured Prediction | ||
| 14 Jan | Maximum Entropy Markov Models versus Conditional Random Fields | Hal vs Piyush |
| 21 Jan | M3Ns versus SVMstruct | _ vs _ |
| 28 Jan | Incremental Perceptron versus Searn | _ vs _ |
| 04 Feb | Dependency Estimation versus Density Estimation | _ vs _ |
| Theory | ||
| 11 Feb | Generalization Bounds versus Structure Compilation | _ vs _ |
| 18 Feb | Negative Results versus Positive Results | _ vs _ |
| 25 Feb | Learning with Hints versus Multiview Learning | _ vs _ |
| Dynamic Programming and Search | ||
| 04 Mar | Approximate A* versus Compiling Comp Ling | _ vs _ |
| 11 Mar | Minimum Spanning Trees versus Matrix-Tree Theorem | _ vs _ |
| 18 Mar | Contrastive Divergence versus Contrastive Estimation | _ vs _ |
| 01 Apr | Searching over Partitions versus End-to-end Machine Translation | _ vs _ |
| Inverse Optimal Control (aka IRL) | ||
| 08 Apr | Learning to Search versus Apprenticeship Learning | _ vs _ |
| 15 Apr | MaxEnt IRL versus Apprenticeship RL | _ vs _ |
| 22 Apr | IO Heuristic Control versus Parsing with IOC | _ vs _ |
Paper Summaries
Additional Optional Reading
Past Semesters
- Fall 2009 (Online Learning)
- Summer 2009 (Assorted Topics)
- Spring 2009 (Learning Theory)
- Fall 2008 (Graphical Models)
- Summer 2008 (Multitask Learning)
- Spring 2008 (Kernel Methods)
- Fall 2007 (Unsupervised Bayes)
- Summer 2007 (Manifolds)
