Corpus ID: 152282688

# Solving Empirical Risk Minimization in the Current Matrix Multiplication Time

@article{Lee2019SolvingER,
title={Solving Empirical Risk Minimization in the Current Matrix Multiplication Time},
author={Y. Lee and Z. Song and Qiuyi Zhang},
journal={ArXiv},
year={2019},
volume={abs/1905.04447}
}
• Published 2019
• Computer Science, Mathematics
• ArXiv
• Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega… CONTINUE READING
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