A (j, k)-coreset for projective clustering is a small set of points that yields a (1 + e)-approximation to the sum of squared distances from the rows of A to any set of k affine subspaces, each of dimension at most j.Expand

We show that every unweighted point set P has a weak (ε, k)-coreset of size Poly(k,1/ε) for the k-means clustering problem, i.e. its size is <i>independent</i> of the cardinality |P| of the point set and the dimension d of the Euclidean space R.Expand

We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output.Expand

We define the notion of private coresets, which are simultaneously both coresets and differentially private, and show how they may be constructed.Expand

We develop efficient (1 + epsiv)-approximation algorithms for generalized facility location problems, and provide efficient algorithms for their construction.Expand

We show that Gaussian mixture models admit coresets of size polynomial in dimension and the number of mixture components, while being independent of the data set size.Expand

In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices.Expand