Machine Learning (Theory)


more icml papers

Tags: Machine Learning,Papers roweis@ 7:02 am

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

One Comments to “more icml papers”
  1. roweis says:

    Just an additional comment, there was a very interesting looking paper by Mike Jordan and Barbara Engelhardt which unfortunately
    overlapped with Marina’s talk. Related to Marina’s talk was a paper by Fernando and Takeo
    Linli Xu had a paper, following up the great stuff from the NIPS workshops
    (convex HMMs!)
    Also on the clustering front, overlapping with Blei’s talk was this paper by Arik and Zoubin

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