Global Convergence and Stability of Stochastic Gradient Descent
Authors: Vivak Patel, Shushu Zhang, Bowen Tian
NeurIPS 2022 | Venue 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 EMAIL Shushu Zhang Department of Statistics University of Michigan Ann Arbor EMAIL Bowen Tian Department of Statistics The Ohio State University EMAIL |
| 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. |