# Shashanka Ubaru

## Contact Details

NameShashanka Ubaru |
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## Pubs By Year |
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## Pub CategoriesComputer Science - Learning (3) Computer Science - Numerical Analysis (2) Mathematics - Numerical Analysis (2) Mathematics - Information Theory (1) Computer Science - Information Theory (1) Computer Science - Data Structures and Algorithms (1) Statistics - Machine Learning (1) |

## Publications Authored By Shashanka Ubaru

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced model selection and estimation. Read More

Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n \times n}$ is a fundamental task in countless applications. In matrix multiplication time, it is possible to perform a full SVD and directly compute the singular values $\sigma_1,.. Read More

In many machine learning and data related applications, it is required to have the knowledge of approximate ranks of large data matrices at hand. In this paper, we present two computationally inexpensive techniques to estimate the approximate ranks of such large matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of finding eigenvalues of the matrix at a given point on the real line. Read More

Low rank approximation is an important tool used in many applications of signal processing and machine learning. Recently, randomized sketching algorithms were proposed to effectively construct low rank approximations and obtain approximate singular value decompositions of large matrices. Similar ideas were used to solve least squares regression problems. Read More