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 [1].
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. |