82 citations · 359 across the 49 of their papers we have counts for
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Sharper Bounds for Regularized Data Fitting
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
We study matrix sketching methods for regularized variants of linear regression, low rank approximation, and canonical correlation analysis. Our main focus is on sketching techniqu…
Faster Kernel Ridge Regression Using Sketching and Preconditioning
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Kernel Ridge Regression (KRR) is a simple yet powerful technique for non-parametric regression whose computation amounts to solving a linear system. This system is usually dense an…
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, Vikas Sindhwani
We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods. The proposed kernel is defined based on a hi…
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations
Insu Han, Dmitry Malioutov, Haim Avron +1
Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (…