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Contents 1 CS7941: Topics in Machine Learning 1.1 Expectations 1.2 Participants 1.3 Schedule 1.4 Paper Summaries 1.5 Additional Optional Reading 1.6 Past Semesters

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	Factorie 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

See Structured Prediction on Braque.

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)