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 theory improved Fréchet mean of symmetric positive definite matrices
Authors: Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frederic Pascal
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation, involving both synthetic and real-world EEG and hyperspectral datasets, shows that we largely outperform stateof-the-art methods. |
| Researcher Affiliation | Collaboration | 1Universit e Paris Saclay, CNRS, Centrale Sup elec, L2S 2Universit e Savoie Mont Blanc, LISTIC 3Huawei Paris Research Center. |
| Pseudocode | Yes | Algorithm 1 Covariance based on RMT corrected distance, Algorithm 2 RMT corrected Fr echet mean on S++ p, Algorithm 3 Nearest Centroid classifier based on RMT, Algorithm 4 K-Means clustering based on RMT |
| Open Source Code | Yes | To ensure reproducibility, the code for the experiments discussed is accessible at https://github.com/ Ammar Mian/icml-rmt-2024. |
| Open Datasets | Yes | motor imagery datasets accessible via the MOABB platform (Aristimunha et al., 2023). |
| Dataset Splits | No | The paper mentions a 'training set' in the context of the Nearest Centroid algorithm, but it does not provide specific train/validation/test split percentages, sample counts, or explicit instructions for how the datasets were partitioned for the empirical evaluations. |
| Hardware Specification | No | The development and evaluation of these methods were conducted in Python. Specifically, SCM and LW implementations were sourced from the scikit-learn library (Pedregosa et al., 2018), while LW-NL comes from scikit-RMT3. The conventional Fr echet means, standard Nearest Centroid and K-Means algorithms were taken from the py Riemann library (Barachant et al., 2023). |
| Software Dependencies | No | The development and evaluation of these methods were conducted in Python. Specifically, SCM and LW implementations were sourced from the scikit-learn library (Pedregosa et al., 2018), while LW-NL comes from scikit-RMT3. The conventional Fr echet means, standard Nearest Centroid and K-Means algorithms were taken from the py Riemann library (Barachant et al., 2023). |
| Experiment Setup | Yes | Parameters are p = 64, K = 10 on the left and n = 128 on the right. (from Figure 1 caption under Simulations) and 'The K-means algorithm, capped at 100 iterations with early stopping at a 10^-4 tolerance'. |