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X-WR-CALNAME:Johns Hopkins Algorithms and Complexity
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X-FROM-URL:https://www.cs.jhu.edu/~mdinitz/theory
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UID:ai1ec-341@www.cs.jhu.edu/~mdinitz/theory
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DESCRIPTION:Speaker: Xuan Wu\nAffiliation: Johns Hopkins University\nTitle:
Coreset for Clustering in Graph Metrics.\nAbstract:\nClustering is a fund
amental task in machine learning. As the increasing demand for running mac
hine learning algorithms in the huge data sets\, classic clustering algori
thms were found not to scale well. To this end\, coreset is introduced as
a powerful data reduction technique that turns a huge dataset into a tiny
proxy. Coresets have been also successfully applied to streaming and distr
ibuted computing. Coresets for clustering in Euclidean spaces have been v
ery well studied. However\, very few results were known about the non-Eucl
idean space. Beyond Euclidean\, graph metrics is a very important family o
f metric space. In this talk\, I will cover my recent work on coreset for
k-clustering in graph metrics\, including bounded treewidth graph and excl
uded-minor graph.
DTSTART;TZID=America/New_York:20201209T120000
DTEND;TZID=America/New_York:20201209T130000
LOCATION:https://wse.zoom.us/j/91450299380
SEQUENCE:0
SUMMARY:[Theory Seminar] Xuan Wu
URL:https://www.cs.jhu.edu/~mdinitz/theory/event/theory-seminar-xuan-wu-2/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nSpeaker: Xuan
Wu

\nAffiliation: Johns Hopkins University

\nTitle: Coreset fo
r Clustering in Graph Metrics.

\nAbstract:

\nClustering is a fu
ndamental task in machine learning. As the increasing demand for running m
achine learning algorithms in the huge data sets\, classic clustering algo
rithms were found not to scale well. To this end\, coreset is introduced a
s a powerful data reduction technique that turns a huge dataset into a tin
y proxy. Coresets have been also successfully applied to streaming and dis
tributed computing. Coresets for clustering in Euclidean spaces have been
very well studied. However\, very few results were known about the non-Eu
clidean space. Beyond Euclidean\, graph metrics is a very important family
of metric space. In this talk\, I will cover my recent work on coreset fo
r k-clustering in graph metrics\, including bounded treewidth graph and ex
cluded-minor graph.

\n
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