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..
Riemannian coordinate descent algorithms on matrix manifolds
Authors: Andi Han, Pratik Jawanpuria, Bamdev Mishra
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments. We now benchmark the performance of the proposed RCD and RCDlin algorithms in terms of computational efficiency (flop counts and/or runtime) and convergence quality (distance to optimality). |
| Researcher Affiliation | Collaboration | 1Riken AIP, Japan 2Microsoft India. Correspondence to: Andi Han <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Riemannian coordinate descent (RCD/RCDlin) |
| Open Source Code | Yes | Our codes are implemented using the Manopt toolbox (Boumal et al., 2014) and run on a laptop with an i5-10500 3.1GHz CPU processor. The codes are available at https://github.com/andyjm3. |
| Open Datasets | Yes | For experiment settings, we train 5-dimensional embeddings (n = 5) for Word Net mammals subtree (Miller, 1998). |
| Dataset Splits | No | The paper describes generating synthetic data for some experiments and using WordNet, but no explicit train/validation/test dataset splits (e.g., percentages, sample counts, or specific predefined splits) are provided for reproducibility of data partitioning. |
| Hardware Specification | Yes | Our codes are implemented using the Manopt toolbox (Boumal et al., 2014) and run on a laptop with an i5-10500 3.1GHz CPU processor. |
| Software Dependencies | No | Our codes are implemented using the Manopt toolbox (Boumal et al., 2014). However, no specific version numbers for Manopt or other software dependencies are provided. |
| Experiment Setup | Yes | For all the methods, we tune the stepsize. ... For RCDlin-c and RCDlin-tc, we use a linearly-decaying stepsize, i.e., η/(1 + 0.1 epoch). For RGD we use a fixed stepsize η which generally leads to better convergence. We tune and set η = 1.0 for RCDlin and 0.5 for RGD. ... We set S = np/5 and select the coordinates randomly without replacement. |