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Oblivious Sketching for Logistic Regression
Alexander Munteanu · Simon Omlor · David Woodruff

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @ None #None
What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regression. Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from $n$ to only $\operatorname{poly}(\mu d\log n)$ weighted points, where $\mu$ is a useful parameter which captures the complexity of compressing the data. Solving (weighted) logistic regression on the sketch gives an $O(\log n)$-approximation to the original problem on the full data set. We also show how to obtain an $O(1)$-approximation with slight modifications. Our sketches are fast, simple, easy to implement, and our experiments demonstrate their practicality.

Author Information

Alexander Munteanu (TU Dortmund)
Simon Omlor (TU Dortmund)
David Woodruff (Carnegie Mellon University)

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