Least-mean squares (LMS) solvers such as Linear/Ridge/Lasso-Regression, SVD and
Elastic-Net not only solve fundamental machine learning problems, but are also the building …

We study active sampling algorithms for linear regression, which aim to query only a small
number of entries of a target vector and output a near minimizer to the objective function. For …

In this work, we propose a sketching-based central path method for solving linear
programmings, whose running time matches the state of art results [Cohen, Lee, Song STOC …

In this work, we propose a sketching-based central path method for solving linear
programmings, whose running time matches the state of the art results [Cohen, Lee, Song …

There are a number of approximation algorithms for NP-hard versions of low rank
approximation, such as finding a rank-$ k $ matrix $ B $ minimizing the sum of absolute …

Expected risk minimization (ERM) is at the core of many machine learning systems. This
means that the risk inherent in a loss distribution is summarized using a single number-its …

We study the column subset selection problem with respect to the entrywise $\ell_1 $-norm
loss. It is known that in the worst case, to obtain a good rank-$ k $ approximation to a matrix …