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..
Linear Regularizers Enforce the Strict Saddle Property
Authors: Matthew Ubl, Matthew Hale, Kasra Yazdani
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This rule is shown to guarantee that gradient descent will escape the neighborhoods around a broad class of non-strict saddle points, and this behavior is demonstrated on numerical examples of nonstrict saddle points common in the optimization literature. |
| Researcher Affiliation | Academia | Matthew Ubl, Matthew Hale, Kasra Yazdani Department of Mechanical and Aerospace Engineering University of Florida, Gainesville, FL, 32611, USA. EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Locally Linearly Regularized Gradient Descent |
| Open Source Code | No | The paper does not mention providing access to the source code for the described methodology. |
| Open Datasets | No | The paper uses mathematical functions (e.g., f(x, y) = 1/3x^3 + 1/2y^2, Inverted Wine Bottle) for numerical examples and demonstrations, not publicly available datasets that would typically have access information. |
| Dataset Splits | No | The paper uses mathematical functions for numerical examples and does not mention any training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We initialize Algorithm 1 at (1, 1) with γ = 1/54 and run using values of θ varying from 0.01 to 1.7 (θ ≈ 1.717 for this function). Each run of the algorithm terminates when f(x) + l < 10^-7. |