Utkarsh Porwal

Utkarsh Porwal
Research Scientist
Biography

Utkarsh works in the search technology group as part of the recall team. He works on automatic query rewrites, entity recognition and structured data. Before joining search science, he was part of the trust science group where he was working on detecting abusive buyers and feature selection. His research interest lies broadly in the areas of information retrieval, pattern recognition and applied machine learning. 

Publications
Advances in Neural Information Processing Systems (NIPS), 2014

Parallel Feature Selection inspired by Group Testing

Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q Ngo, Long Nguyen, Christopher Ré, Venu Govindaraju

This paper presents a parallel feature selection method for classification that scales up to very high dimensions and large data sizes. Our original method is inspired by group testing theory, under which the feature selection procedure consists of a collection of randomized tests to be performed in parallel. Each test corresponds to a subset of features, for which a scoring function may be applied to measure the relevance of the features in a classification task. We develop a general theory providing sufficient conditions under which true features are guaranteed to be correctly identified. Superior performance of our method is demonstrated on a challenging relation extraction task from a very large data set that have both redundant features and sample size in the order of millions. We present comprehensive comparisons with state-of-the-art feature selection methods on a range of data sets, for which our method exhibits competitive performance in terms of running time and accuracy. Moreover, it also yields substantial speedup when used as a pre-processing step for most other existing methods.

Patents