982 citations · 2.8k across the 131 of their papers we have counts for
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Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations
Insu Han, Dmitry Malioutov, Haim Avron +1
Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (…
Minimum Weight Perfect Matching via Blossom Belief Propagation
Sungsoo Ahn, Sejun Park, Michael Chertkov +1
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Mode…
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions
Insu Han, Dmitry Malioutov, Jinwoo Shin
Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partit…
Scalable Iterative Algorithm for Robust Subspace Clustering
Sanghyuk Chun, Yung-Kyun Noh, Jinwoo Shin
Subspace clustering (SC) is a popular method for dimensionality reduction of high-dimensional data, where it generalizes Principal Component Analysis (PCA). Recently, several metho…
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles
Sungsoo Ahn, Michael Chertkov, Andrew E. Gelfand +2
We study the Maximum Weight Matching (MWM) problem for general graphs through the max-product Belief Propagation (BP) and related Linear Programming (LP). The BP approach provides…