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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Authors: Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabas Poczos
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our focus is theoretical, we also present experiments that illustrate our theoretical findings. |
| Researcher Affiliation | Academia | Simon S. Du Carnegie Mellon University EMAIL Chi Jin University of California, Berkeley EMAIL Jason D. Lee University of Southern California EMAIL Michael I. Jordan University of California, Berkeley EMAIL Barnabás Póczos Carnegie Mellon University EMAIL Aarti Singh Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Perturbed Gradient Descent [Jin et al., 2017] |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper defines an objective function for its experiments (equations 14 and 15 in the Appendix) rather than using a publicly available dataset. Therefore, no concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper uses a custom-defined objective function for its experiments, not a standard dataset. Therefore, no information on training/validation/test splits is provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions parameters like stepsize, tthres, gthres, and r, but does not specify any software names with version numbers used for the experiments. |
| Experiment Setup | Yes | For both GD and PGD we let the stepsize η = 1 4L. For PGD, we choose tthres = 1, gthres = γe 100 and r = e 100. In Figure 3 we fix dimension d = 5 and vary L as considered in Section 4.1; similarly in Figure 4 we choose d = 10 and vary L. |