Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Nonparanormal Information Estimation
Authors: Shashank Singh, BarnabĆ”s PĆ³czos
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 19,339 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,783 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 Ī£ was drawn by normalizing a random covariance matrix from a Wishart distribution, and data X1, ..., Xn i.i.d. N(0, Ī£) 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 speciļ¬ed otherwise, each experiment had the following basic structure: In each trial, a correlation matrix Ī£ was drawn by normalizing a random covariance matrix from a Wishart distribution, and data X1, ..., Xn i.i.d. N(0, Ī£) drawn. All 5 estimators were computed from X1, ..., Xn and squared error from true mutual information (computed from Ī£) was recorded. Unless speciļ¬ed otherwise, n = 100 and D = 25. For IĻ and IĻ, 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. |