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Found 9 papers, 3 papers with code
Learning Gaussian Graphical Models with Latent Confounders
In this paper, we compare and contrast two strategies for inference in graphical models with latent confounders: Gaussian graphical models with latent variables (LVGGM) and PCA-based removal of confounding (PCA+GGM).
Endogenous Representation of Asset Returns
Our results point to the possibility that most of the risk in equity markets may be explained by a sparse network of interacting assets (or their issuing firms).
Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes
Next, the partial separability structure is shown to be particularly useful in order to provide a well-defined functional Gaussian graphical model that can be identified with a sequence of finite-dimensional graphical models, each of identical fixed dimension.
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption.
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
As a consequence of this formulation, the radius of the Wasserstein ambiguity set is directly related to the regularization parameter in the estimation problem.
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data.
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists.
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
In direct contrast to the parallel work in the Gaussian setting however, this new convex pseudo-likelihood framework has not leveraged the extensive array of methods that have been proposed in the machine learning literature for convex optimization.
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees
As none of the popular methods proposed for solving pseudo-likelihood based objective functions have provable convergence guarantees, it is not clear if corresponding estimators exist or are even computable, or if they actually yield correct partial correlation graphs.
