Three-Toed Sloth

Triple Header (Next Week at the Statistics / Machine Learning Seminars)

Attention conservation notice: Only relevant if you (1) really care about statistics, and (2) will be in Pittsburgh on Monday.

Through a fortuitous concourse of calendars, we will have three outstanding talks on Monday, 14 October 2013. In chronological order:

Michael I. Jordan, "On the Computational and Statistical Interface and 'Big Data'" (special joint statistics/ML seminar)

Abstract: The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the statistical and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. Indeed, if data are a data analyst's principal resource, why should more data be burdensome in some sense? Shouldn't it be possible to exploit the increasing inferential strength of data at scale to keep computational complexity at bay? I present three research vignettes that pursue this theme, the first involving the deployment of resampling methods such as the bootstrap on parallel and distributed computing platforms, the second involving large-scale matrix completion, and the third introducing a methodology of "algorithmic weakening," whereby hierarchies of convex relaxations are used to control statistical risk as data accrue.

(Joint work with Venkat Chandrasekaran, Ariel Kleiner, Lester Mackey, Purna Sarkar, and Ameet Talwalkar.)

Time and place: Noon, Rangos 2, University Center

David Choi, "Testing for Coordination and Peer Influence in Network Data" (machine learning and the social sciences seminar)

Abstract: Many tests have been proposed for the detection of "viral" peer influence in observational studies involving social network data. However, these tests typically make strong (and sometimes unstated) modeling assumptions on participant behavior. We propose a test which holds under less restrictive assumptions, and which controls for unobserved homophily variables that are unaccounted for in existing methods. We discuss conditions under which the test is valid, and give preliminary results on its effectiveness.

Time and place: 3 pm in Gates Hall 4405

Genevera Allen, "Sparse and Functional Principal Components Analysis"

Abstract: Regularized principal components analysis, especially Sparse PCA and Functional PCA, has become widely used for dimension reduction in high-dimensional settings. Many examples of massive data, however, may benefit from estimating both sparse AND functional factors. These include neuroimaging data where there are discrete brain regions of activation (sparsity) but these regions tend to be smooth spatially (functional). Here, we introduce an optimization framework that can encourage both sparsity and smoothness of the row and/or column PCA factors. This framework generalizes many of the existing approaches to Sparse PCA, Functional PCA and two-way Sparse PCA and Functional PCA, as these are all special cases of our method. In particular, our method permits flexible combinations of sparsity and smoothness that lead to improvements in feature selection and signal recovery as well as more interpretable PCA factors. We demonstrate our method on simulated data and a neuroimaging example of EEG data. This work provides a unified optimization framework for regularized PCA that can form the foundation for a cohesive approach to regularization in high-dimensional multivariate analysis.

Time and place: 4 pm in Doherty Hall 1212

As always, the talks are free and open to the public.

Enigmas of Chance; Networks