Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Inexact trust-region algorithms on Riemannian manifolds
Authors: Hiroyuki Kasai, Bamdev Mishra
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,861 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,516 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evaluations demonstrate that the proposed algorithms outperform state-of-the-art Riemannian deterministic and stochastic gradient algorithms across different applications. |
| Researcher Affiliation | Collaboration | Hiroyuki Kasai The University of Electro-Communications Japan EMAIL Bamdev Mishra India EMAIL |
| Pseudocode | Yes | Algorithm 1 Inexact Riemannian trust-region (Inexact RTR) algorithm |
| Open Source Code | Yes | The implementation of the proposed algorithms uses the MATLAB toolbox Manopt [40] and is available at https://github.com/hiroyuki-kasai/Subsampled-RTR. |
| Open Datasets | Yes | We use three real-world datasets: Yale B [46], COIL-100 [47], and CIFAR-100 [48]... two real-world datasets with r = 10, where Case P3 deals with the MNIST dataset [52]... deals with the Covertype dataset [53]... Jester dataset 1 [54] |
| Dataset Splits | No | The paper does not explicitly mention training/validation/test dataset splits, percentages, or absolute counts for validation. |
| Hardware Specification | Yes | All simulations are performed in MATLAB on a 4.0 GHz Intel Core i7 machine with 32 GB RAM. |
| Software Dependencies | No | The paper mentions 'MATLAB toolbox Manopt' but does not provide specific version numbers for either MATLAB or Manopt. |
| Experiment Setup | Yes | By following [38], we set |Sg| = n/10 and |SH| = n/102 except Cases P5, P6, M4, and M5. We set the batch-size to n/10 in RSVRG. All simulations are performed in MATLAB on a 4.0 GHz Intel Core i7 machine with 32 GB RAM. |