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Paolo Pellizzoni, Andrea Pietracaprina and Geppino Pucci
Metric k-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so a more sensible variant seeks for the best solution that disregards a given number z of points ...
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Xin Yao, Juan Yu, Jianmin Han, Jianfeng Lu, Hao Peng, Yijia Wu and Xiaoqian Cao
Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks o...
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Sinead A. Williamson and Jette Henderson
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean discrep...
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Artem Barger and Dan Feldman
Let P be a set of n points in R??
R
d
, ??=1
k
=
1
be an integer and ???(0,1)
e
?
(
0
,
1
)
be a constant. An e-coreset is a subset ?????
C
?
P
with appropriate non-negative weights (scalars), that approximates any given set ???R??
Q
?
R
d
of k cente...
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Dror Epstein and Dan Feldman
We suggest a provable and practical approximation algorithm for fitting a set P of n points in Rd" role="presentation">R??Rd
R
d
to a sphere. Here, a sphere is represented by its center x∈Rd" role="presentation">???R??x?Rd
x
?
R
d
and radius...
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