more icml papers

Here are a few other papers I enjoyed from ICML06.

Topic Models:

• 
  Dynamic Topic Models
  David Blei, John Lafferty
  A nice model for how topics in LDA type models can evolve over time,
  using a linear dynamical system on the natural parameters and a very
  clever structured variational approximation (in which the mean field
  parameters are pseudo-observations of a virtual LDS). Like all Blei
  papers, he makes it look easy, but it is extremely impressive.
• 
  Pachinko Allocation
  Wei Li, Andrew McCallum
  A very elegant (but computationally challenging) model which induces
  correlation amongst topics using a multi-level DAG whose interior nodes
  are “super-topics” and “sub-topics” and whose leaves are the
  vocabulary words. Makes the slumbering monster of structure learning stir.

Sequence Analysis (I missed these talks since I was chairing another session)

• 
  Online Decoding of Markov Models with Latency Constraints
  Mukund Narasimhan, Paul Viola, Michael Shilman
  An “ah-ha!” paper showing how to trade off latency and decoding
  accuracy when doing MAP labelling (Viterbi decoding) in sequential
  Markovian models. You’ll wish you thought of this yourself.
• 
  Efficient inference on sequence segmentation model
  Sunita Sarawagi
  A smart way to re-represent potentials in segmentation models
  to reduce the complexity of inference from cubic in the input sequence
  to linear. Also check out her NIPS2004 paper with William Cohen
  on “segmentation CRFs”. Moral of the story: segmentation is NOT just
  sequence labelling.

Optimal Partitionings/Labellings

• 
  The uniqueness of a good optimum for K-means
  Marina Meila
  Marina shows a stability result for K-means clustering, namely
  that if you find a “good” clustering it is not too “different” than the
  (unknowable) optimal clustering and that all other good clusterings
  are “near” it. So, don’t worry about local minima in K-means as long
  as you get a low objective.
• 
  Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation
  Pradeep Ravikumar, John Lafferty
  Paradeep and John introduce QP relaxations for the problem of finding
  the best joint labelling of a set of points (connected by a weighted
  graph and with a known metric cost between labels and extended
  the non-metric case). Surprisingly, they show that the QP relaxation
  is both computationally more attractive and more accurate than
  the “natural” LP relaxation or than loopy BP approximations.

Distinguished Paper Award Winners

• 
  How Boosting the Margin Can Also Boost Classifier Complexity
  Lev Reyzin, Robert Schapire
• 
  Trading Convexity for Scalability
  Ronan Collobert, Fabian Sinz, Jason Weston, Leon Bottou
• 
  Looping Suffix Tree-Based Inference of Partially Observable Hidden State
  Michael Holmes, Charles Isbell