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..
Faster Gradient-Free Methods for Escaping Saddle Points
Authors: Hualin Zhang, Bin Gu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct several numerical experiments to verify the effectiveness of the proposed methods for escaping saddle points and the efficiency compared with the existing methods. |
| Researcher Affiliation | Academia | Hualin Zhang 1, Bin Gu 1,2 1Nanjing University of Information Science & Technology 2MBZUAI EMAIL |
| Pseudocode | Yes | Algorithm 1 Zeroth-Order Perturbed Accelerated Gradient Descent, Algorithm 2 Negative Curvature Exploitation (xt, vt, s), Algorithm 3 Zeroth-Order Perturbed Accelerated Gradient Descent with Accelerated Negative Curvature Finding, Algorithm 4 Zeroth-Order Accelerated Negative Curvature Finding without Renormalization( x, r , T ) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the methodology described. |
| Open Datasets | No | The paper evaluates its methods on a 'cubic regularization problem' and a 'quartic function', both of which are mathematical formulations or synthetic problems, not publicly available datasets with specific access information. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. It appears to use synthetic functions initialized from a saddle point. |
| Hardware Specification | Yes | All experiments are performed on a computer with a six-core Intel Core i5-10500 CPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for its software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | In this experiment, we set Ο΅ = 10 2. ... For Algorithm 1 and 3, the parameter settings basically follow Eq. (5) and Eq. (7). Specifically, we choose Ο΅ = 0.001 and the perturbation radius r and r are set to 0.001. The Lipschitz constants βand Ο are selected based on a coarse grid search of the region {0.1, 1, 10, 100} {0.1, 1, 10, 100}. Table 2: Parameter settings of the cubic regularization problem experiment. Table 3: Parameter settings of the cubic quartic function experiment. |