[Ml-stat-talks] Fwd: Wilks Statistics Seminar: Chen Xu, Monday, April 10, 2017 12:30 PM, Sherrerd Hall 101

Barbara Engelhardt bee at princeton.edu
Tue Apr 4 09:27:28 EDT 2017

Talk of interest.

***   Wilks Statistics Seminar   ***

DATE: Monday, April 10, 2017      *<== Note different day*

TIME:   12:30 pm

LOCATION:   Sherrerd Hall 101

SPEAKER:  Chen Xu, University of Ottawa

TITLE:   Distributed Kernel Regression for Large-scale Data

ABSTRACT:  In modern scientific research, massive datasets with huge
numbers of observations are frequently encountered. To facilitate the
computational process, a divide-and-conquer scheme is often used for the
analysis of big data. In such a strategy, a full dataset is first split
into several manageable segments; the final output is then aggregated from
the individual outputs of the segments. Despite its popularity in practice,
it remains largely unknown that whether such a distributive strategy
provides valid theoretical inferences to the original data; if so, how
efficient does it work? In this talk, I address these fundamental issues
for the non-parametric distributed kernel regression, where accurate
prediction is the main learning task. I will begin with the naive simple
averaging algorithm and then talk about an improved approach via ADMM. The
promising preference of these methods is supported by both simulation and
real data examples.
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