Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
Authors: Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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. |