Learning Local Neighborhoods of Non-Gaussian Graphical Models
Authors: Sarah Liaw, Rebecca Morrison, Youssef Marzouk, Ricardo Baptista
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm in both Gaussian and non-Gaussian settings by comparing it to existing methods. Lastly, we show the scalability of the proposed approach by applying it to high-dimensional non-Gaussian examples, including a biological dataset with more than 150 variables. Numerical Results We now aim to answer the following questions: (1) Can L-SING accurately quantify the conditional dependencies of X without relying on assumptions about the distribution of X? (2) Is L-SING computationally tractable for highdimensional problems? The first and second experiments address question (1), while the second and third experiments address question (2). Additionally, we compare the performance of ˆΩL-SING to existing methods on the same test dataset (see the ar Xiv version for detailed experimental setups). |
| Researcher Affiliation | Academia | 1California Institute of Technology 2University of Colorado Boulder 3Massachusetts Institute of Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: L-SING Algorithm |
| Open Source Code | Yes | The code to reproduce the numerical experiments is available at: https://github.com/Sarah Liaw/L-SING. |
| Open Datasets | Yes | Finally, we address question (2) by demonstrating the scalability of L-SING on the high-dimensional curated Ovarian Data (Ganzfried et al. 2013), comprising gene expression profiles from 578 ovarian cancer patients sourced from The Cancer Genome Atlas (TCGA). |
| Dataset Splits | Yes | the final dataset included 156 genes (variables) and 578 samples, split into 346 training, 117 evaluation, and 115 validation samples. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for running the experiments. The paper only mentions computation times in seconds. |
| Software Dependencies | No | The paper mentions UMNN (Unconstrained Monotonic Neural Networks) as a method and refers to algorithms like GLASSO and Lasso. However, it does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Figure 5a shows the estimated generalized precision matrix ˆΩfor r = 5 pairs (d = 10), as computed using UMNN map components with [64, 64, 64] hidden layers and M = 5, 000 training samples. To show the scalability of L-SING, Figure 5b presents ˆΩfor r = 20 pairs (d = 40), using M = 5, 000 training samples and the same UMNN architecture. Figure 8a presents ˆΩ, as computed using UMNN map components with [64, 128, 128] hidden layers. |