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..
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
Authors: Zijian Liu, Zhengyuan Zhou
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Ethics Statement: This is a theory work. Hence, there are no potential ethics concerns. Reproducibility Statement: We include the full proofs of all theorems in the appendix. |
| Researcher Affiliation | Academia | Zijian Liu , Zhengyuan Zhou Stern School of Business, New York University EMAIL |
| Pseudocode | Yes | Algorithm 1 Composite Stochastic Mirror Descent (CSMD) Input: x1 X, ηt > 0, t [T]. for t = 1 to T do xt+1 = argminx X h(x) + bgt, x xt + Dψ(x,xt) ηt Return x T +1 |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper is found. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |