66 citations · 72 across the 4 of their papers we have counts for
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Near-Optimal Entrywise Sampling of Numerically Sparse Matrices
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan +1
Many real-world data sets are sparse or almost sparse. One method to measure this for a matrix is the \emph{numerical sparsity}, denoted $\mathsf{ns}(…
Competitively Pricing Parking in a Tree
Max Bender, Jacob Gilbert, Aditya Krishnan +1
Motivated by demand-responsive parking pricing systems we consider posted-price algorithms for the online metrical matching problem and the online metrical searching problem in a t…
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan +1
Spectral functions of large matrices contains important structural information about the underlying data, and is thus becoming increasingly important. Many times, large matrices re…
On Sketching the to norms
Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff
We initiate the study of data dimensionality reduction, or sketching, for the norms. Given an matrix , the norm, denoted $\|A\|_{q \to p} = \sup_{…