NIPS Workshop on Perturbations, Optimization and Statistics

 

We are happy to announce the 2012 NIPS Workshop on Perturbations, Optimization and Statistics, to be held on December 7 or 8 in Lake Tahoe, Nevada. More information about the workshop is available at the website.

In nearly all machine learning tasks, we expect there to be randomness, or noise, in the data we observe and in the relationships encoded by the model. Usually, this noise is considered undesirable, and we would eliminate it if possible. However, there is an emerging body of work on perturbation methods, showing the benefits of explicitly adding noise into the modeling, learning, and inference pipelines. This workshop will bring together the growing community of researchers interested in different aspects of this area, and will broaden our understanding of why and how perturbation methods can be useful.

More generally, perturbation methods usually provide efficient and principled ways to reason about the neighborhood of possible outcomes when trying to make the best decision. For example, some might want to arrive at the best outcome that is robust to small changes in model parameters. Others might want to find the best choice while compensating for their lack of knowledge by averaging over the different outcomes. Recently, several works influenced by diverse fields of research such as statistics, optimization, machine learning, and theoretical computer science, use perturbation methods in similar ways. The goal of this workshop is to explore different techniques in perturbation methods and their consequences on computation, statistics and optimization.

Contributed papers are welcome. Please see the workshop website for more information on submission. The deadline is 30 September 2012.