On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
Authors: Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically validate our proposed identifiability results, we carry out experiments under various settings. We also conduct ablation studies to verify the necessity of the proposed assumptions and include Fast ICA [23] as a representative baseline. |
| Researcher Affiliation | Academia | Ignavier Ng 1, Yujia Zheng 1, Xinshuai Dong1, Kun Zhang1,2 1 Carnegie Mellon University 2 Mohamed bin Zayed University of Artificial Intelligence {ignavierng, yujiazh, dongxinshuai, kunz1}@cmu.edu |
| Pseudocode | Yes | Algorithm 1 Decomposition-Based Method |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using a third-party implementation (L-BFGS from Sci Py) but no link or statement about their own code release. |
| Open Datasets | No | The paper states it "simulate[s] 10 sources" and generates data parameters randomly. There is no concrete access information (link, DOI, repository, or formal citation) for this simulated data to be publicly available. |
| Dataset Splits | No | The paper discusses experiments with 'different sample sizes' and '1000 samples' but does not specify exact training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states: 'We run each of the experiments on 12 CPUs and 8 GBs of memory.' This provides a general count for CPUs and memory, but lacks specific model numbers or detailed computer specifications for reproducibility. |
| Software Dependencies | Yes | In our experiments, we use the L-BFGS algorithm [11] implemented in Sci Py [44] to solve each unconstrained optimization problem of quadartic penalty method. |
| Experiment Setup | Yes | For the sparsity term ρ(A), we use MCP with hyperparameters λ = 1, α = 40 and λ = 0.1, α = 10 for decomposition-based and likelihood-based methods, respectively. For quadratic penalty method, we use c1 = 10 5 and c1 = 10 2 for decomposition-based and likelihood-based methods, respectively, and use β = 1.5 for both methods. Lastly, we also use a threshold of 0.01 to remove small weights in the estimated mixing matrix. Typically, we have t = 250 for each L-BFGS run, and 125 iterations for the quadratic penalty method. |