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Found 9 papers, 1 papers with code
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations.
Foundational principles for large scale inference: Illustrations through correlation mining
Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed.
Two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)
In the first stage we collect a few ($n$) expensive samples $\{y_i,\mathbf x_i\}_{i=1}^n$, at the full dimension $p\gg n$ of $\mathbf X$, winnowing the number of variables down to a smaller dimension $l < p$ using a type of cross-correlation or regression coefficient screening.
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.
G-AMA: Sparse Gaussian graphical model estimation via alternating minimization
Over and above estimating a sparse inverse covariance matrix, we also illustrate how to (1) incorporate constraints on the (bivariate) correlations and, (2) incorporate equality (equisparsity) or linear constraints between individual inverse covariance elements.
Duality in Graphical Models
First, we discuss the pairwise and global Markov properties for undirected and bidirected models, using the pseudographoid and reverse-pseudographoid rules which are weaker conditions than the typically used intersection and composition rules.
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.
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design.
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
In this paper, a proximal gradient method (G-ISTA) for performing L1-regularized covariance matrix estimation is presented.
