We design new algorithms for k-clustering in high-dimensional Euclidean
...
k-Clustering in ℝ^d (e.g., k-median and k-means) is a
fundamental machin...
We consider a generalized poset sorting problem (GPS), in which we are g...
We study the power of uniform sampling for k-Median in various metric
sp...
Designing small-sized coresets, which approximately preserve the costs
o...
Max-Cut is a fundamental problem that has been studied extensively in va...
We consider robust clustering problems in ℝ^d, specifically
k-clustering...
The method of random Fourier features (RFF), proposed in a seminal paper...
Motivated by practical generalizations of the classic k-median and
k-mea...
In Euclidean Uniform Facility Location, the input is a set of clients in...
We provide nearly optimal algorithms for online facility location (OFL) ...
We devise the first coreset for kernel k-Means, and use it to obtain new...
We provide the first coreset for clustering points in ℝ^d that
have mult...
We consider a natural generalization of the Steiner tree problem, the St...
Coresets are modern data-reduction tools that are widely used in data
an...
We initiate the study of coresets for clustering in graph metrics, i.e.,...
In a recent work, Chierichetti et al. studied the following "fair" varia...
We design coresets for Ordered k-Median, a generalization of classical
c...
We study the problem of constructing ε-coresets for the (k,
z)-clusterin...