Graph-generative

Arsh’s PhD Defense - Relational Structure Discovery for Deep Learning

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Arshdeep Sekhon’s PhD Defense June 29, 2022.

DrQi’s tutorial talk about jointnets at UCLA computational genomics summer school 2019 for extracting connectomes from heterogeneous samples

less than 1 minute read

Here is the slide of my tutorial talk at UCLA computational genomics summer school 2019.

JointNets R package for Joint Network Estimation, Visualization, Simulation and Evaluation from Heterogeneous Samples

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jointNets R package: a Suite of Fast and Scalable Tools for Learning Multiple Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowle...

kDIFFNet - Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

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Tool kDIFFNet: Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

A Series of Tutorials We wrote to explain the JointS GM tools we built for extracting connectomes from heterogeneous samples

less than 1 minute read

So far, we have released the following Tutorials:

Beilun’s PhD Defense - Fast and Scalable Joint Estimators for Learning Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowledge

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PhD Defense Presentation by Beilun Wang Friday, July 20, 2018 at 9:00 am in Rice 242 Committee Members: Mohammad Mahmoody (Chair), Yanjun Qi (Advisor), ...

ICML - JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

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Tool JEEK: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

AISTAT - DIFFEE to identify Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

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Tool DIFFEE: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

NeurIPS - W-SIMULE

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Tool W-SIMULE: A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs with Additional Prior knowled...

AISTAT - FASJEM R package is released!

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Tool FASJEM: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

Journal Machine Learning - SIMULE R package is released!

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Tool SIMULE: A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models

NeurIPS- Learning the Dependency Structure of Latent Factors

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NeurIPS - A few other MRF tools we built

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Paper1: Learning the Dependency Structure of Latent Factors Y. He, Y. Qi, K. Kavukcuoglu, H. Park (2012) NeurIPS PDF Talk: Slide