Nonparanormal Information Estimation
Authors: Shashank Singh, Barnabás Póczos
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <sss1@andrew.cmu.edu>. |
| 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. |