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dist(x,y) andavg(A,B) = 1 |A| |B| X

Neural Information Processing Systems

In this paper, we present a comprehensive study of the performance of average-link in metric spaces, regarding several natural criteria that capture separability and cohesion, and aremore interpretable than Dasgupta'scost function and itsvariants.







Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss

Neural Information Processing Systems

Nevertheless, we show that under certain minimal and realistic distributional settings, it is possible to obtain a (1+ null)-approximation with a nearly linear running time and poly (k/null) + O ( k log n) columns. Namely, we show that if the input matrix A has the form A = B + E, where B is an arbitrary rank-k matrix, and E is a matrix with i.i.d.