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A central problem in data streams is to characterize which functions of an underlying frequency vector can be approximated efficiently.
Approximate nearest neighbors: towards removing the curse of dimensionality
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Special Functions
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An information statistics approach to data stream and communication complexity
Z. Bar-Yossef, T. S. Jayram, R. Kumar, and D. Sivakumar · 2002
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Tabulation based 4-universal hashing with applications to second moment estimation
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Weighted random sampling with a reservoir
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Asymptotically Optimal Lower Bounds on the NIH-Multi-Party Information Complexity of the AND-Function and Disjointness
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A sparse johnson: Lindenstrauss transform
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Almost optimal explicit Johnson-Lindenstrauss families
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The streaming complexity of cycle counting, sorting by reversals, and other problems
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Eigenvalues of a matrix in the streaming model
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Optimal bounds for Johnson-Lindenstrauss transforms and streaming problems with subconstant error
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Sparser Johnson-Lindenstrauss transforms
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On sketching matrix norms and the top singular vector
Y. Li, H. L. Nguyen, and D. P. Woodruff · 2014
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Distributed estimation of generalized matrix rank: Efficient algorithms and lower bounds
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Efficient estimation of eigenvalue counts in an interval
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