5 Result(s)
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Chapter
Data Visualization Through Their Graph Representations
The amount of data and information collected and retained by organizations and businesses is constantly increasing, due to advances in data collection, computerization of transactions, and breakthroughs in sto...
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Article
Operator-valued kernel-based vector autoregressive models for network inference
Reverse-engineering of high-dimensional dynamical systems from time-course data still remains a challenging and important problem in knowledge discovery. For this learning task, a number of approaches primaril...
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Chapter and Conference Paper
Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information
Reconstructing directed acyclic graphs (DAGs) from observed data constitutes an important machine learning task. It has important applications in systems biology and functional genomics. However, it is a chall...
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Chapter and Conference Paper
Analyses of Multi-collection Corpora via Compound Topic Modeling
Popular probabilistic topic models have typically centered on one single text collection, which is deficient for comparative text analyses. We consider a setting where we have partitionable corpora. Each subco...
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Article
A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions
The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradig...
