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..
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
Authors: David Martı́nez-Rubio, Christophe Roux, Sebastian Pokutta
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we explore beyond our theory with empirical tests. ... 5. Experiments: Numerical tests exploring beyond our theory. We observe that RGD presents a monotonic decrease in distance to an optimizer and show that RIPPA is competitive. |
| Researcher Affiliation | Academia | 1Zuse Institute Berlin, Germany 2Technische Universit at Berlin, Germany. |
| Pseudocode | Yes | Algorithm 1 Riemannian Inexact Proximal Point Algorithm (RIPPA) |
| Open Source Code | No | The paper mentions using the Pymanopt library (Townsend et al., 2016), published under the BSD-3-Clause license. This indicates the use of an open-source library, but the paper does not state that the authors are releasing their own code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data ('centers yi') for the Karcher mean problem, but does not refer to a publicly available or open dataset with concrete access information (link, DOI, repository, or formal citation). |
| Dataset Splits | No | The paper describes a problem setup for the Karcher mean but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'Pymanopt library (Townsend et al., 2016)' but does not specify its version number or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | We implement RGD with step sizes η = 1/L and η = 1/(LζR) as well as RIPPA performing a constant number of iterations of PRGD to approximately solve the proximal problems. ... We performed 3 iterations in each subroutine. ... We run until a fixed precision is reached in function value, and because of this, different algorithms stop at points at different distances from x*. ... In fact, we ran the algorithms for different settings, different initializations, and we performed a grid search on the step sizes. |