Machine Learning (Theory)

4/15/2013

NEML II

Adam Kalai points out the New England Machine Learning Day May 1 at MSR New England. There is a poster session with abstracts due April 19. I understand last year’s NEML went well and it’s great to meet your neighbors at regional workshops like this.

12/29/2012

Simons Institute Big Data Program

Tags: Announcements,Funding,Workshop jl@ 8:17 am

Michael Jordan sends the below:

The new Simons Institute for the Theory of Computing
will begin organizing semester-long programs starting in 2013.

One of our first programs, set for Fall 2013, will be on the “Theoretical Foundations
of Big Data Analysis”. The organizers of this program are Michael Jordan (chair),
Stephen Boyd, Peter Buehlmann, Ravi Kannan, Michael Mahoney, and Muthu Muthukrishnan.

See http://simons.berkeley.edu/program_bigdata2013.html for more information on
the program.

The Simons Institute has created a number of “Research Fellowships” for young
researchers (within at most six years of the award of their PhD) who wish to
participate in Institute programs, including the Big Data program. Individuals
who already hold postdoctoral positions or who are junior faculty are welcome
to apply, as are finishing PhDs.

Please note that the application deadline is January 15, 2013. Further details
are available at http://simons.berkeley.edu/fellows.html .

Mike Jordan

10/26/2012

ML Symposium and Strata/Hadoop World

Tags: Conferences,Workshop jl@ 11:40 am

The New York ML symposium was last Friday. There were 303 registrations, up a bit from last year. I particularly enjoyed talks by Bill Freeman on vision and ML, Jon Lenchner on strategy in Jeopardy, and Tara N. Sainath and Brian Kingsbury on deep learning for speech recognition. If anyone has suggestions or thoughts for next year, please speak up.

I also attended Strata + Hadoop World for the first time. This is primarily a trade conference rather than an academic conference, but I found it pretty interesting as a first time attendee. This is ground zero for the Big data buzzword, and I see now why. It’s about data, and the word “big” is so ambiguous that everyone can lay claim to it. There were essentially zero academic talks. Instead, the focus was on war stories, product announcements, and education. The general level of education is much lower—explaining Machine Learning to the SQL educated is the primary operating point. Nevertheless that’s happening, and the fact that machine learning is considered a necessary technology for industry is a giant step for the field. Over time, I expect the industrial side of Machine Learning to grow, and perhaps surpass the academic side, in the same sense as has already occurred for chip design. Amongst the talks I could catch, I particularly liked the Github, Zillow, and Pandas talks. Ted Dunning also gave a particularly masterful talk, although I have doubts about the core Bayesian Bandit approach(*). The streaming k-means algorithm they implemented does look quite handy.

(*) The doubt is the following: prior elicitation is generally hard, and Bayesian techniques are not robust to misspecification. This matters in standard supervised settings, but it may matter more in exploration settings where misspecification can imply data starvation.

10/18/2012

7th Annual Machine Learning Symposium

A reminder that the New York Academy of Sciences will be hosting the 7th Annual Machine Learning Symposium tomorrow from 9:30am.

The main program will feature invited talks from Peter BartlettWilliam Freeman, and Vladimir Vapnik, along with numerous spotlight talks and a poster session. Following the main program, hackNY and Microsoft Research are sponsoring a networking hour with talks from machine learning practitioners at NYC startups (specifically bit.ly, Buzzfeed, Chartbeat, and Sense Networks, Visual Revenue). This should be of great interest to everyone considering working in machine learning.

8/27/2012

NYAS ML 2012 and ICML 2013

The New York Machine Learning Symposium is October 19 with a 2 page abstract deadline due September 13 via email with subject “Machine Learning Poster Submission” sent to physicalscience@nyas.org. Everyone is welcome to submit. Last year’s attendance was 246 and I expect more this year.

The primary experiment for ICML 2013 is multiple paper submission deadlines with rolling review cycles. The key dates are October 1, December 15, and February 15. This is an attempt to shift ICML further towards a journal style review process and reduce peak load. The “not for proceedings” experiment from this year’s ICML is not continuing.

Edit: Fixed second ICML deadline.

« Newer PostsOlder Posts »

Powered by WordPress