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.
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:See the full call for papers for more details and submission information.
- 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?
(I remember when Han took stochastic processes from me --- how can he be organizing workshops?)
Posted at March 04, 2011 01:44 | permanent link