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..
Nonparanormal Information Estimation
Authors: Shashank Singh, Barnabás Póczos
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Empirical Results |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, Pittsburgh, USA. Correspondence to: Shashank Singh <EMAIL>. |
| Pseudocode | No | The paper describes the methods verbally but does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code can be found on Git Hub4. https://github.com/sss1/nonparanormal-information |
| Open Datasets | No | The paper generates synthetic data for its experiments, rather than using a pre-existing publicly available dataset. 'In each trial, a correlation matrix Σ was drawn by normalizing a random covariance matrix from a Wishart distribution, and data X1, ..., Xn i.i.d. N(0, Σ) drawn.' |
| Dataset Splits | No | The paper describes generating synthetic i.i.d. samples but does not specify train/validation/test splits as it's not a typical machine learning training setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB source code is available', but does not specify the version of MATLAB or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Except as specified otherwise, each experiment had the following basic structure: In each trial, a correlation matrix Σ was drawn by normalizing a random covariance matrix from a Wishart distribution, and data X1, ..., Xn i.i.d. N(0, Σ) drawn. All 5 estimators were computed from X1, ..., Xn and squared error from true mutual information (computed from Σ) was recorded. Unless specified otherwise, n = 100 and D = 25. For Iρ and Iτ, we used a regularization constant z = 10 3. For Ik NN, except as noted in Experiment 3, k = 2, based on recent analysis (Singh & P oczos, 2016b) suggesting that small values of k are best for estimation. |