Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Authors: Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
NeurIPS 2015 | Venue 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 EMAIL Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ EMAIL Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ EMAIL Guang Cheng Department of Statistics Purdue University West Lafayette, IN EMAIL |
| 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). |