Global Convergence and Stability of Stochastic Gradient Descent
Authors: Vivak Patel, Shushu Zhang, Bowen Tian
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Then, we develop novel theory to address this shortcoming in two ways. First, we establish that SGD s iterates will either globally converge to a stationary point or diverge under nearly arbitrary nonconvexity and noise models. |
| Researcher Affiliation | Academia | Vivak Patel Department of Statistics University of Wisconsin Madison Madison, WI 53706 vivak.patel@wisc.edu Shushu Zhang Department of Statistics University of Michigan Ann Arbor shushuz@umich.edu Bowen Tian Department of Statistics The Ohio State University tian.837@buckeyemail.osu.edu |
| Pseudocode | No | The paper describes the SGD rule mathematically (θk+1 = θk Mk f(θk, Xk+1)) but does not provide it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any statements or links indicating the provision of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with data, so there are no datasets used in the context of training for which access information would be provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with data, so there are no dataset splits for validation described. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental setup or 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. |