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Creators/Authors contains: "Srinivasa, Rakshith Sharma"

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  1. ; ; (, Proeedings of the International Workshop on Artificial Intelligence and Statistics)
    We consider sketched approximate matrix multiplication and ridge regression in the novel setting of localized sketching, where at any given point, only part of the data matrix is available. This corresponds to a block diagonal structure on the sketching matrix. We show that, under mild conditions, block diagonal sketching matrices require only 𝑂(\sr/𝜖2) and 𝑂(\sd𝜆/𝜖) total sample complexity for matrix multiplication and ridge regression, respectively. This matches the state-of-the-art bounds that are obtained using global sketching matrices. The localized nature of sketching considered allows for different parts of the data matrix to be sketched independently and hence is more amenable to computation in distributed and streaming settings and results in a smaller memory and computational footprint. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, 2019 13th International conference on Sampling Theory and Applications (SampTA))
    Full Text Available 
  3. ; ; ; (, Advances in Neural Information Processing Systems 32 (NIPS 2019))
    Accepted Manuscript Full Text Available