Identification of Linear Latent Variable Model with Arbitrary Distribution
Authors: Zhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang, Ruichu Cai6350-6357
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the correctness of the identifiability result and the effectiveness of the proposed method through empirical studies. In this section, we verify the effectiveness of our proposed in both synthetic data and real-world data. |
| Researcher Affiliation | Academia | 1School of Computer Science, Guangdong University of Technology, Guangzhou, China 2School of Mathematical Sciences, Peking University, Beijing, China 3Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA 4 Peng Cheng Laboratory, Shenzhen, Guangdong, China 5 College of Science, Shantou University, Shantou, Guangdong, China |
| Pseudocode | Yes | Algorithm 1: LLCS-AD; Algorithm 2: Orient by transitivity of non-Gaussian noise (ONG) |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In this section, we applied our method on a real-world dataset collected by Byrne (2016) that investigates the impact of organizational (self-esteem, classroom climate) and personality (self-esteem, external locus of control) on three facets of burnout in full-time elementary teachers. |
| Dataset Splits | No | The paper describes generating synthetic data and applying a real-world dataset, but it does not specify explicit train/validation/test splits, percentages, or sample counts for either dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Hilbert-Schmidt Independence Criterion (HSIC) test (Gretton et al. 2005)' but does not specify any version numbers for HSIC or any other software dependencies. |
| Experiment Setup | No | The paper describes the data generation process and evaluation metrics but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or specific training configurations for their proposed algorithm. |