In addition to Ed Snelson’s paper, there were (at least) two other papers that caught my eye at UAI.<\/p>\n
One was this paper<\/a> 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.<\/p>\n The other paper which I found intriguing but which I just haven’t groked yet is this beast<\/a> by Manfred and Dima Kuzmin. 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 … <\/p>\n
\nYou can check out the (beautiful) slides<\/a>
\nif 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.<\/p>\n","protected":false},"excerpt":{"rendered":"