Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
Authors: Zijian Liu, Zhengyuan Zhou
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 37,204 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,922 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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, Ρt > 0, t [T]. for t = 1 to T do xt+1 = argminx X h(x) + bgt, x xt + DĪ(x,xt) Ρt 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. |