### Structured Sparsity: Learning and Inference (Dept. of Signal Amplification)

In statistics, we say that a high-dimensional model is "sparse" if most of
the large numbers of variables do not actually contribute to the outcome ---
the true set of relevant predictors is small compared to the number of
covariates. Some of the most interesting work in statistics and machine
learning over the last decade and a half has been about finding and using
sparsity, often starting from ideas like
the lasso, but
becoming considerably more general and flexible, and connecting to ideas
about compressed
sensing. (I will probably never get around to writing a post
about SpAM, but may yet turn it
into a homework problem; I still have hopes
about TESLA.) Exploiting sparsity
is one of the principal ways of lifting the curse of dimensionality, which
otherwise weighs on us more and more every year.

Now comes
great-looking workshop
on structured sparsity at ICML,
organized by Francis
Bach, Mladen
Kolar, Han
Liu, Guillaume Obozinski
and Eric Xing:

The aim of the workshop is to bring together theory and practice in modeling
and exploring structure in high-dimensional data. Participation of researchers
working on methodology, theory and applications, both from the frequentist and
Bayesian point of view is strongly encouraged in order to discuss different
approaches for tackling challenging high-dimensional problems. Furthermore, the
workshop will link with the signal processing community, which has worked on
similar topics and with whom exchanges of ideas will be very fruitful. We
encourage genuine interaction between proponents of different approaches and
hope to better understand possibilities for modeling of structure in high
dimensional data.
We invite submissions on various aspects of structured sparse modeling in high-dimensions. Here is an example of two key questions:
- How can we automatically learn the hidden structure from the data?
- Once the structure is learned or pre-given, how can we utilize the structure to conduct more effective inference?

See the

full call for papers for more details and submission
information.

(I remember when Han took stochastic processes from me ---
how can he be organizing workshops?)

Enigmas of Chance;
Signal Amplification

Posted at March 04, 2011 01:44 | permanent link