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Creators/Authors contains: "Parulekar, Aditya"

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  1. ; ; ; ; (, ICML 2024)
    Accepted Manuscript Full Text Available
  2. ; ; (, RANDOM)
    We consider the problem of finding an approximate solution to ℓ1 regression while only observing a small number of labels. Given an n×d unlabeled data matrix X, we must choose a small set of m≪n rows to observe the labels of, then output an estimate βˆ whose error on the original problem is within a 1+ε factor of optimal. We show that sampling from X according to its Lewis weights and outputting the empirical minimizer succeeds with probability 1−δ for m>O(1ε2dlogdεδ). This is analogous to the performance of sampling according to leverage scores for ℓ2 regression, but with exponentially better dependence on δ. We also give a corresponding lower bound of Ω(dε2+(d+1ε2)log1δ). 
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    Accepted Manuscript Full Text Available