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..
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
Authors: Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various applications support our ๏ฌndings. |
| Researcher Affiliation | Collaboration | Andi Han1, Bamdev Mishra2, Pratik Jawanpuria2, Junbin Gao1 1The University of Sydney, Australia 2Microsoft, India |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Table 1 summarizes components, but it is not an algorithm. |
| Open Source Code | Yes | The code can be found at https://github.com/andyjm3/AI-vs-BW. |
| Open Datasets | Yes | Here, we test on a dataset included in the Mix Est package [30]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or clear citations to predefined splits). |
| Hardware Specification | Yes | The experiments are conducted in Matlab using the Manopt toolbox [17] on a i5-10500 3.1GHz CPU processor. |
| Software Dependencies | No | The paper mentions 'Matlab' and 'Manopt toolbox [17]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the batch size to be 50 and consider a decaying step size, with the best initialized step size shown in Figures 3(d)&(e). |