Machine Learning (Theory)


Two more UAI papers of interest

Tags: Machine Learning,Papers roweis@ 5:08 am

In addition to Ed Snelson’s paper, there were (at least) two other papers that caught my eye at UAI.

One was this paper by Sanjoy Dasgupta, Daniel Hsu and Nakul Verma at UCSD which shows in a surprisingly general and strong way that almost all linear projections of any jointly distributed vector random variable with finite first and second moments look sphereical and unimodal (in fact look like a scale mixture of Gaussians). Great result, as you’d expect from Sanjoy.

The other paper which I found intriguing but which I just haven’t groked yet is this beast by Manfred and Dima Kuzmin.
You can check out the (beautiful) slides
if that helps. I feel like there is something deep here, but my brain is too small to understand it. The COLT and last NIPS papers/slides are also on Manfred’s page. Hopefully someone here can illuminate.

3 Comments to “Two more UAI papers of interest”
  1. Anonymous says:

    Afraid I can’t help you much with the content (size of brain and all) but I can at least point out that the links in the story are messed up !

    Thanks for pointing out interesting-looking papers btw.

  2. jl says:

    Thanks, I fixed Sam’s http stutter.

  3. I wonder how stuff in “the beast” relates to the notion of free probability (e.g., Free probability and the von Neumann algebras of free groups). Although I haven’t studied both papers, it looks similar.

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