NIPS 2010 - NIPS 2010 Workshop on Robust Statistical Learning

Robust Statistical Learning
Workshop at the 24th Annual Conference on Neural Information
Processing Systems (NIPS 2010)
http://www.cs.utexas.edu/~sai/robustml
Submission Deadline: Nov 09, 2010


There has been a resurgence of robust learning methods (broadly
understood) in recent years, largely from different communities that
rarely interact: (classical) robust statistics, adversarial machine
learning, robust optimization, and multi-structured or dirty model
learning. This workshop aims to bring together researchers from these
different communities, and identify potential common intuitions
underlying such robust learning methods. We are interested in
understanding where techniques from one field might be applicable, and
what their limitations are. As one very important example, we will
consider the high dimensional regime, where it is not clear how to
extend many of the techniques successful in the classical robust
statistics setup. There has been a massive amount of recent interest
and work in modeling such high-dimensional data, and the natural
extension of such results would be to make them more robust. Indeed,
with increasingly high-dimensional and "dirty" real world data that do
not conform to clean modeling assumptions, this is a vital necessity.

We would like to encourage high quality submissions of short papers
relevant to the workshop. Accepted papers will be presented as
spotlight talks and posters. Of particular interest are papers in the
following topics:

(a) Dirty Models: these invoke a combination of structural assumptions
such as sparsity, low-rank etc. to develop a robust estimation method.

(b) Robust Optimization: these use techniques from convexity and
duality, to construct solutions that are immunized from some bounded
level of uncertainty, typically expressed as bounded (but otherwise
arbitrary, i.e., adversarial) perturbations of the decision
parameters.

(c) Classical Robust Statistics; Adversarial Learning: these are
robust to misspecified modeling assumptions in general, and do not
model the outliers specifically.

Submission deadline: Nov 09, 2010
Length & Format: max. 6 pages NIPS 2010 format
Time & Location: December 10 2010, Whistler, Canada
Submission instructions: Via email to robustml.nips-AT-gmail.com
Organizers: Pradeep Ravikumar, Constantine Caramanis, Sujay Sanghavi
(UT Austin)