On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
Authors: Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various applications support our findings. |
| 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). |