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.
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Authors: Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber, Wei Biao Wu
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 34,875 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,211 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 | This paper contributes to theoretical advancements for understanding constant learning-rate SGD and its averaged variant (ASGD) in the challenging high-dimensional regime. |
| Researcher Affiliation | Academia | Jiaqi Li Department of Statistics University of Chicago Chicago, IL 60637 EMAIL Zhipeng Lou Department of Mathematics University of California, San Diego La Jolla, CA 92093 EMAIL Johannes Schmidt-Hieber Department of Applied Mathematics University of Twente Enschede, Netherlands EMAIL Wei Biao Wu Department of Statistics University of Chicago Chicago, IL 60637 EMAIL |
| Pseudocode | No | The paper describes algorithms like SGD and ASGD using mathematical equations (e.g., Eq. 2 and 3) and textual descriptions, but does not present them in structured pseudocode or an algorithm block format. |
| Open Source Code | No | This paper contributes to theoretical guarantees of high-dimensional SGD. This paper does not include experiments requiring code. |
| Open Datasets | No | This paper contributes to theoretical guarantees of high-dimensional SGD. This paper does not include experiments requiring code. |
| Dataset Splits | No | This paper contributes to theoretical guarantees of high-dimensional SGD. This paper does not include experiments requiring code. |
| Hardware Specification | No | This paper contributes to theoretical guarantees of high-dimensional SGD. This paper does not include experiments requiring code. |
| Software Dependencies | No | This paper contributes to theoretical guarantees of high-dimensional SGD. This paper does not include experiments requiring code. |
| Experiment Setup | No | This paper does not include experiments, therefore, no experimental setup details are provided. |