March 29, 2012

Sparsity as Sorcery (Next Two Weeks at the Statistics Seminar)

Attention conservation notice: Only of interest if you (1) care about high-dimensional statistics and (2) will be in Pittsburgh over the next two weeks.

I am not sure how our distinguished speakers would feel at being called sorcerers, but since one of them is using sparsity to read minds, and the other to infer causation from correlation, it is hard to think of a more appropriate word.

Bin Yu, "Sparse Modeling: Unified Theory and Movie Reconstruction Based on Brain Signal"
Abstract: Information technology has enabled the collection of massive amounts of data in science, engineering, social science, finance and beyond. Statistics is the science of data and indispensable for extracting useful information from high-dimensional data. After broad successes of statistical machine learning on prediction through regularization, interpretability is gaining attention and sparsity is used as its proxy. With the virtues of both regularization and sparsity, L1 penalized Least Squares (e.g. Lasso) has been intensively studied by researchers from statistics, applied mathematics, and signal processing. Lasso is a special case of sparse modeling and has also been the focus on compressive sensing lately.
In this talk, I would like to cover both theory and practice of Lasso and its extensions. First, I will present an insightful unified analysis of M-estimation with decomposable penalties under sparse high dimensional statistical models. Second, I will present collaborative research with the Gallant Neuroscience Lab at Berkeley on understanding human visual pathway. In particular, I will show how we use non-linear sparse models (SPAM) to improve encoding and decoding results for the visual cortex area V1, and I will explain how Lasso and ridge methods enter our movie reconstruction algorithm from fMRI brain signals (dubbed by TIME Magazine as "mind-reading computers" and selected as one of its 50 Best Inventions of 2011).
Time and place: 4--5 pm on Monday, 2 April 2012, in Scaife Hall 125
Peter Bühlmann, "Predicting Causal Effects in High-Dimensional Settings"
Abstract: Understanding cause-effect relationships between variables is of great interest in many fields of science. An ambitious but highly desirable goal is to infer causal effects from observational data obtained by observing a system of interest without subjecting it to interventions. This would allow to circumvent severe experimental constraints or to substantially lower experimental costs. Our main motivation to study this goal comes from applications in biology.
We present recent progress for prediction of causal effects with direct implications on designing new intervention experiments, particularly for high-dimensional, sparse settings with thousands of variables but based on only a few dozens of observations. We highlight exciting possibilities and fundamental limitations. In view of the latter, statistical modeling needs to be complemented with experimental validations: we discuss this in the context of molecular biology for yeast (Saccharomyces cerevisiae) and the model plant Arabidopsis thaliana.
Time and place: 4--5 pm on Wednesday, 11 April 2012, in Scaife Hall 125

As always, the talks are free and open to the public; hecklers will, however, be turned into newts.

Enigmas of Chance; Minds, Brains, Neurons

Posted at March 29, 2012 13:10 | permanent link

Three-Toed Sloth