MLRG/spring10

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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.

Participants

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

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