
A simple 7/3approximation algorithm for feedback vertex set in tournaments
We show that performing just one round of the SheraliAdams hierarchy gi...
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Locally Private kMeans Clustering with Constant Multiplicative Approximation and NearOptimal Additive Error
Given a data set of size n in d'dimensional Euclidean space, the kmean...
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An improved approximation algorithm for ATSP
We revisit the constantfactor approximation algorithm for the asymmetri...
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Constant factor FPT approximation for capacitated kmedian
Capacitated kmedian is one of the few outstanding optimization problems...
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A constant FPT approximation algorithm for hardcapacitated kmeans
Hardcapacitated kmeans (HCKM) is one of the fundamental problems remai...
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Differential Private Hogwild! over Distributed Local Data Sets
We consider the Hogwild! setting where clients use local SGD iterations ...
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Decode efficient prefix codes
Data compression is used in a wide variety of tasks, including compressi...
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Locally Private kMeans in One Round
We provide an approximation algorithm for kmeans clustering in the oneround (aka noninteractive) local model of differential privacy (DP). This algorithm achieves an approximation ratio arbitrarily close to the best non private approximation algorithm, improving upon previously known algorithms that only guarantee large (constant) approximation ratios. Furthermore, this is the first constantfactor approximation algorithm for kmeans that requires only one round of communication in the local DP model, positively resolving an open question of Stemmer (SODA 2020). Our algorithmic framework is quite flexible; we demonstrate this by showing that it also yields a similar nearoptimal approximation algorithm in the (oneround) shuffle DP model.
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