[Ml-stat-talks] Fwd: Reminder: CSML Seminar: Emily Fox, Tuesday, April 12, 2016 at 12:30pm-Green Hall, Room 0-S-6

Barbara Engelhardt bee at princeton.edu
Wed Apr 6 13:58:49 EDT 2016


Talk of interest next week.

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Emily Fox, University of Washington

CSML Seminar

Tuesday, April 12, 2016

12:30pm-1:30pm

Green Hall, Room 0-S-6

**Lunch will be provided**



Title: “Scalable Bayesian Models of Interacting Time Series”



Abstract: Data streams of increasing complexity and scale are being
collected in a variety of fields ranging from neuroscience, genomics, and
environmental monitoring to e-commerce.  Modeling the intricate and
possibly evolving relationships between the large collection of series can
lead to increased predictive performance and domain-interpretable
structures.  For scalability, it is crucial to discover and exploit sparse
dependencies between the data streams.  Such representational structures
for independent data sources have been studied extensively, but have
received limited attention in the context of time series.  In this talk, we
present a series of Bayesian models for capturing such sparse dependencies
via clustering, graphical models, and low-dimensional embeddings of time
series.   We explore these methods in a variety of applications, including
house price modeling and inferring networks in the brain.



We then turn to observed interaction data, and briefly touch upon how to
devise statistical network models that capture important network features
like sparsity of edge connectivity.  Within our Bayesian framework, a key
insight is to move to a continuous-space representation of the graph,
rather than the typical discrete adjacency matrix structure.  We
demonstrate our methods on a series of real-world networks with up to
hundreds of thousands of nodes and millions of edges.





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