Sub-sampled Cubic Regularization for Non-convex Optimization
Authors: Jonas Moritz Kohler, Aurelien Lucchi
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 confirm 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 <jonas.kohler@student.kit.edu>, Aurelien Lucchi <aurelien.lucchi@inf.ethz.ch>. |
| 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. |