Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Oblivious sketching for logistic regression   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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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: math
8
The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th