The Information Structuralist

Two public service announcements

Posted in Information Theory, Public Service Announcements by mraginsky on February 25, 2014

1. The 2014 IEEE North American Summer School on Information Theory will take place June 18-21, 2014 at the Fields Institute in Toronto, Canada.

2. For those of you who use Matlab or Octave, there is a new Information Theoretical Estimators (ITE) toolbox, an open-source toolbox “capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions.” Some more details are available in the guest post by the toolbox’s creator, Zoltán Zsabó, at the Princeton Information Theory b-log.

It’s for a good cause!

Posted in Information Theory, Public Service Announcements by mraginsky on May 2, 2013

Endorse the petition to honor Claude Elwood Shannon with a United States Postal Service stamp on the 100th anniversary of his birth.

Public Service Announcement: Princeton-Stanford Information Theory b-log

Posted in Information Theory, Public Service Announcements by mraginsky on January 3, 2013

Sergio Verdú has started a brand new Information Theory b-log that should be of interest to the readers of this blog. The ‘About’ page says:

Wel­come to the Princeton-Stanford Infor­ma­tion The­ory b-log! All researchers work­ing on infor­ma­tion the­ory are invited to par­tic­i­pate by post­ing items to the blog. Both orig­i­nal mate­r­ial and point­ers to the web are welcome.


COST: NIPS 2011 Workshop on Computational Trade-offs in Statistical Learning

Posted in Optimization, Public Service Announcements, Statistical Learning and Inference by mraginsky on September 1, 2011

Alekh Agarwal and Sasha Rakhlin are organizing a workshop at this year’s NIPS. I’m on the program committee, so it is my duty (and distinct pleasure) to invite you all to peruse the full call for papers here, or at least to check out this key snippet:

We would like to welcome high-quality submissions on topics including but not limited to:

  • Fundamental statistical limits with bounded computation
  • Trade-offs between statistical accuracy and computational costs
  • Computation-preserving reductions between statistical problems
  • Algorithms to learn under budget constraints
  • Budget constraints on other resources (e.g. bounded memory)
  • Computationally aware approaches such as coarse-to-fine learning

Interesting submissions in other relevant topics not listed above are welcome too. Due to the time constraints, most accepted submissions will be presented as poster spotlights.

Oh, and did I mention that the workshop will take place in mid-December in Sierra Nevada, Spain?