In cut sparsification, all cuts of a hypergraph H=(V,E,w) are approximat...
We design new algorithms for k-clustering in high-dimensional Euclidean
...
Many streaming algorithms provide only a high-probability relative
appro...
We study the classical metric k-median clustering problem over a set of
...
The edit distance between strings classically assigns unit cost to every...
Max-Cut is a fundamental problem that has been studied extensively in va...
We study the problem of high-dimensional sparse linear regression in a
d...
Motivated by practical generalizations of the classic k-median and
k-mea...
Given a large edge-capacitated network G and a subset of k vertices
call...
In vertex-cut sparsification, given a graph G=(V,E) with a terminal set
...
We study vertex sparsification for distances, in the setting of planar g...
In Euclidean Uniform Facility Location, the input is a set of clients in...
Matrix sparsification is a well-known approach in the design of efficien...
Given a graph with non-negative edge weights, there are various ways to
...
We study the problem of approximating edit distance in sublinear time. T...
In 1961, Gomory and Hu showed that the max-flow values of all n 2
pairs ...
We devise new cut sparsifiers that are related to the classical
sparsifi...
We devise the first coreset for kernel k-Means, and use it to obtain new...
We consider an approximate version of the trace reconstruction
problem, ...
We provide the first coreset for clustering points in ℝ^d that
have mult...
We design an n^2+o(1)-time algorithm that constructs a cut-equivalent
(G...
Graph sparsification has been studied extensively over the past two deca...
Large matrices are often accessed as a row-order stream. We consider the...
We consider the problem of sparse normal means estimation in a distribut...
Every undirected graph G has a (weighted) cut-equivalent tree T, commonl...
Cut and spectral sparsification of graphs have numerous applications,
in...
We consider a natural generalization of the Steiner tree problem, the St...
Many real-world data sets are sparse or almost sparse. One method to mea...
We study approximation algorithms for variants of the median string
prob...
Min-Cut queries are fundamental: Preprocess an undirected edge-weighted
...
Coresets are modern data-reduction tools that are widely used in data
an...
We consider the rooted orienteering problem in Euclidean space: Given n
...
The edit distance is a way of quantifying how similar two strings are to...
We investigate for which metric spaces the performance of distance label...
The spectrum of a matrix contains important structural information about...
We initiate the study of coresets for clustering in graph metrics, i.e.,...
Orthogonal Matching pursuit (OMP) is a popular algorithm to estimate an
...
We study algorithms for the sliding-window model, an important variant o...
We design coresets for Ordered k-Median, a generalization of classical
c...
We investigate the time-complexity of the All-Pairs Max-Flow
problem: Gi...
Most known algorithms in the streaming model of computation aim to
appro...
The relationship between the sparsest cut and the maximum concurrent
mul...
We study sublinear algorithms that solve linear systems locally. In
the ...
We reprove three known algorithmic bounds for terminal-clustering proble...
We reprove three known (algorithmic) bounds for terminal-clustering prob...
We introduce a batch version of sparse recovery, where the goal is to
re...
Given a directed graph, the vertex connectivity from u to v is the
maxim...
In the Set Cover problem, the input is a ground set of n elements and a
...
Estimating the leading principal components of data, assuming they are
s...
Recent advances in large-margin classification of data residing in gener...