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..
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Authors: Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 provides experimental validations and applications, including an improved version of LDA/QDA based on the proposed enhanced estimates. sections 4.1. Validation on synthetic data and 4.2. Application to LDA/QDA contain figures and tables demonstrating empirical results. |
| Researcher Affiliation | Academia | 1Centrale Sup elec, University Paris Saclay, France 2GIPSAlab, University Grenoble-Alpes, France 3LISTIC, University Savoie Mont-Blanc, France. |
| Pseudocode | Yes | Algorithm 1 Proposed estimation algorithm. and Algorithm 2 Improved estimation from linear shrinkage. |
| Open Source Code | Yes | Matlab codes for the proposed estimation algorithms are available at https://github. com/maliktiomoko/RMTCov Est and are based on Manopt, a Matlab toolbox for optimization on manifolds (Boumal et al., 2014). |
| Open Datasets | Yes | The bottom right displays are applications to EEG data from (Andrzejak et al., 2001). |
| Dataset Splits | No | The paper mentions 'training vectors' for LDA/QDA but does not explicitly provide specific train/validation/test dataset splits or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | Yes | Matlab codes for the proposed estimation algorithms are available at https://github. com/maliktiomoko/RMTCov Est and are based on Manopt, a Matlab toolbox for optimization on manifolds (Boumal et al., 2014). |
| Experiment Setup | No | The paper describes the gradient descent algorithm, step size 't' (fixed or optimized by backtracking line search), and initializations (M0 = Ip or linear shrinkage), but it does not provide specific numerical values for hyperparameters like fixed step size or details for the backtracking line search. |