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arXiv · 2107.06615

Oblivious sketching for logistic regression

Alexander Munteanu·Simon Omlor·David Woodruff
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Abstract

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.

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BibTeXRIS

Alexander Munteanu, Simon Omlor, David Woodruff. 2021-07-14. Oblivious sketching for logistic regression. https://arxiv.org/abs/2107.06615

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