Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Authors: Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are backed by thorough numerical studies. Finally, we conduct extensive experiments to evaluate the numerical performance of the proposed alternating minimization method. |
| Researcher Affiliation | Collaboration | Wei Sun Yahoo Labs Sunnyvale, CA sunweisurrey@yahoo-inc.com Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ zhaoran@princeton.edu Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ hanliu@princeton.edu Guang Cheng Department of Statistics Purdue University West Lafayette, IN chengg@stat.purdue.edu |
| Pseudocode | Yes | Algorithm 1 Solve sparse tensor graphical model via Tensor lasso (Tlasso) |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing the source code for the methodology described. |
| Open Datasets | Yes | Also, in the example of microarray study for aging [3], thousands of gene expression measurements are recorded on 16 tissue types on 40 mice with varying ages, which forms a four way gene-tissue-mouse-age tensor. [3] J. Zahn, S. Poosala, A. Owen, D. Ingram, et al. AGEMAP: A gene expression database for aging in mice. PLOS Genetics, 3:2326 2337, 2007. |
| Dataset Splits | No | The paper describes different scenarios for simulations (s1, s2, s3) based on sample size (n) and dimensions (m_k), but it does not specify any training, validation, or test dataset splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'glasso algorithm [21]' and 'huge package [29]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In our Tlasso algorithm we set the initialization of k-th precision matrix as 1mk for each k = 1, . . . , K and the total iteration T = 1. The tuning parameter λk is set as 20 log mk/(nmmk). |