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STAT Seminar Series: Massive-scale Sparse Inverse Covariance Matrix estimation: a data science perspective

Speaker Name: 
Sang Yun-Oh
Speaker Title: 
Assistant Professor
Speaker Organization: 
UC Santa Barbara
Start Time: 
Monday, May 20, 2019 - 4:00pm
End Time: 
Monday, May 20, 2019 - 5:00am


Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. HP-CONCORD method is a highly scalable optimization method for estimating a sparse inverse covariance matrix based on a regularized pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal gradient method uses a novel communication-avoiding linear algebra algorithm and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving parallel scalability on problems with up to ≈819 billion parameters (1.28 million dimensions); even on a single node, HP-CONCORD demonstrates scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to estimate the underlying dependency structure of the brain from fMRI data and use the result to identify functional regions automatically.