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Sparse Inverse Covariance Estimation Paper Published in NIPS Site

Today, I found that the work  “A Block-Coordinate Descent Approach for Large-Scale Sparse Inverse Covariance Estimation” joint with Eran Treister was published in the NIPS 2014 proceedings website. I will publish the algorithm code for this work and the Multilevel framework in a few days.

Hope that you enjoy it and please send me your comments!

NIPS 2014 – Accepted!

A few days ago, I’ve received the notification about the acceptance to NIPS 2014 of the work I submitted with my friend and colleague Eran Treister back in June. The NIPS 2014 conference will be held in Montreal, Canada during December 8th and 11th.  Our work is about a new algorithm to solve the Sparse Inverse Covariance Estimation problem in high dimensions, such that the memory is a limitation factor. In the work we show that the algorithm is faster than the previous methods in thousands to millions of variables, and that the algorithm is capable of running in a single server with 64GB because of its reduced memory usage.

2nd Prize in the CS Research Day 2014

The poster that I presented about the new work with my friend and colleague Eran Treister, obtained the 2nd place in the CS Faculty Research Day. The event was held last Monday at CS faculty building. Among visitors there were undergrad students, professors, and industry people.

The work presents a state-of-the-art method to compute the sparse inverse of the covariance matrix in huge dimensions (hundred thoudsands elements). The method allows for computation of a 100K by 100K matrix in about 10 hours in a quad core computer with 8Gb memory.