Wednesday Oct 9, 12pm-1pm, CODA 1315
GLAD: Learning Sparse Graph Recovery
Advisor: Prof. Srinivas Aluru
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an l1 regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formulation to recover the graph structure. Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix. However, it is a challenging task in this case, since the symmetric positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in learned algorithms, and a direct mapping from data to precision matrix may contain many parameters. We propose a deep learning architecture, GLAD, which uses an Alternating Minimization (AM) algorithm as our model inductive bias, and learns the model parameters via supervised learning. We show that GLAD learns a very compact and effective model for recovering sparse graphs from data.
Harsh is a 4th year Machine Learning PhD student. He is advised by Prof. Srinivas Aluru and also mentored by Prof. Le Song. He likes working with Probabilistic Graphical Models and developing structured Deep Learning architectures which can be used for wide variety of tasks. He got his undergraduate degree in Electronics & ECE from Indian Institute of Technology Kharagpur. He is always up for a squash game and is an ardent admirer of bad puns!