Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
Authors: Xiwen WANG, Jiaxi Ying, Daniel Palomar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The synthetic and real-world experiments demonstrate that our proposed method presents a significant speed-up compared to the state-of-the-art benchmarks. |
| Researcher Affiliation | Academia | Xiwen Wang1, Jiaxi Ying1,2 , Daniel P. Palomar1 The Hong Kong University of Science and Technology1 HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute2 {xwangew, jx.ying}@connect.ust.hk, palomar@ust.hk |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | 2Codes are available in https://github.com/Xiwen1997/mtp2-bbd. |
| Open Datasets | Yes | We consider learning the MTP2 GGM for the Crop Image dataset available from the UCR Time Series Archive [56]. |
| Dataset Splits | No | The paper does not explicitly provide train/validation/test dataset splits with specific percentages, counts, or references to predefined splits for the main experiments. For the real-world dataset, it mentions using 'the first 10 observations. The remaining 36 observations were used to calculate the out-of-sample log-likelihood' for a specific test on MTP2 assumption, but this is not the general experimental setup for model training and evaluation. |
| Hardware Specification | Yes | All experiments were conducted on 2.60GHZ Xeon Gold 6348 machines and Linux OS. |
| Software Dependencies | No | All methods are implemented in MATLAB and the state-of-the-art methods we consider includes BCD: Block Coordinate Descent [19]... (no version numbers provided for MATLAB or any specific libraries). |
| Experiment Setup | Yes | We begin with an underlying graph that has an adjacency matrix A Sp, and define Θ = δI A, where δ = 1.05 λmax (A) and λmax (A) represents the largest eigenvalue of A. ... We then sample n = 10p data points from a Gaussian distribution N(0, Θ 1) and calculate the sample covariance matrix as S. ... we set Λij = χ (Θ(0) ij + ϵ) when i = j and Λij = 0 when i = j. Here, χ > 0 determines the sparsity level and ε is a small positive constant, such as 10 3. ... For the real-world experiment, The regularization matrix Λ is determined using the approach in Section 4.1 with ϵ = 0.01 and χ = 0.2. |