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..
Sub-sampled Cubic Regularization for Non-convex Optimization
Authors: Jonas Moritz Kohler, Aurelien Lucchi
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experimental results In this section we present experimental results on real-world datasets where n d 1. They largely conο¬rm the analysis derived in the previous section. Please refer to the Appendix for more detailed results and experiments on higher dimensional problems. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Switzerland. Correspondence to: Jonas Moritz Kohler <EMAIL>, Aurelien Lucchi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Sub-sampled Cubic Regularization (SCR) |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided, nor does it include a link to a code repository. |
| Open Datasets | Yes | We ran experiments on the datasets a9a, covtype and higgs (see details in the appendix). |
| Dataset Splits | No | The paper mentions using datasets but does not explicitly provide specific training/validation/test dataset splits or percentages needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper states that 'More details concerning the choice of the hyperparameters are provided in the appendix', but these details are not present in the main text. |