1.3k citations · 2.1k across the 14 of their papers we have counts for
15 papers
Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
Chau Tran, Pedro Cisneros-Velarde, Sang-Yun Oh +1
Recently, a special case of precision matrix estimation based on a distributionally robust optimization (DRO) framework has been shown to be equivalent to the graphical lasso. From…
Learning Gaussian Graphical Models with Latent Confounders
Ke Wang, Alexander Franks, Sang-Yun Oh
Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology to finance. In practice, data is often corrupted by lat…
Endogenous Representation of Asset Returns
Zhipu Zhou, Alexander Shkolnik, Sang-Yun Oh
Factor modeling of asset returns has been a dominant practice in investment science since the introduction of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theor…
Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes
Javier Zapata, Sang-Yun Oh, Alexander Petersen
The covariance structure of multivariate functional data can be highly complex, especially if the multivariate dimension is large, making extensions of statistical methods for stan…
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
Pedro Cisneros-Velarde, Sang-Yun Oh, Alexander Petersen
Building on a recent framework for distributionally robust optimization, we consider estimation of the inverse covariance matrix for multivariate data. We provide a novel notion of…
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad +5
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfor…
A convex framework for high-dimensional sparse Cholesky based covariance estimation
Kshitij Khare, Sang Oh, Syed Rahman +1
Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of…
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
Alnur Ali, Kshitij Khare, Sang-Yun Oh +1
We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, thro…
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Evan Racah, Seyoon Ko, Peter Sadowski +5
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientis…
Towards a sparse, scalable, and stably positive definite (inverse) covariance estimator
Sang-Yun Oh, Bala Rajaratnam, Joong-Ho Won
High dimensional covariance estimation and graphical models is a contemporary topic in statistics and machine learning having widespread applications. An important line of research…
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Sang-Yun Oh, Onkar Dalal, Kshitij Khare +1
Sparse high dimensional graphical model selection is a popular topic in contemporary machine learning. To this end, various useful approaches have been proposed in the context of $…
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees
Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam
Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelih…
Cosmology from Maxima-1, Boomerang and COBE/DMR CMB Observations
A. H. Jaffe, P. A. R. Ade, A. Balbi +39
Recent results from BOOMERANG-98 and MAXIMA-1, taken together with COBE-DMR, provide consistent and high signal-to-noise measurements of the CMB power spectrum at spherical harmoni…
MAXIMA-1: A Measurement of the Cosmic Microwave Background Anisotropy on angular scales of 10 arcminutes to 5 degrees
S. Hanany, P. Ade, A. Balbi +19
We present a map and an angular power spectrum of the anisotropy of the cosmic microwave background (CMB) from the first flight of MAXIMA. MAXIMA is a balloon-borne experiment with…
MAXIMA: an experiment to measure temperature anisotropy in the cosmic microwave background
A. T. Lee, P. Ade, A. Balbi +21
We describe the MAXIMA experiment, a balloon-borne measurement designed to map temperature anisotropy in the Cosmic Microwave Background (CMB) over a wide range of angular scales (…