Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Authors: Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report preliminary experimental results to verify our theory.In this subsection, we report preliminary experimental results to validate our theoretical results. We consider two datasets available at the LIBSVM website: svmguide3 and w5a (Chang & Lin, 2011). |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK 2Department of Mathematics and Statistics, State University of New York at Albany, USA 3Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA. |
| Pseudocode | No | The paper describes algorithmic updates such as '( wt+1 = Proj W wt ηt wf(wt, vt; zit) , vt+1 = Proj V vt + ηt vf(wt, vt; zit) . (4.1)' but does not present them in a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | The source codes are available at https://github.com/zhenhuan-yang/minimax-stability. |
| Open Datasets | Yes | We consider two datasets available at the LIBSVM website: svmguide3 and w5a (Chang & Lin, 2011). |
| Dataset Splits | No | The paper describes the datasets used for experiments but does not provide details on training, validation, or test splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We consider step sizes ηt = η/ T with η {0.1, 0.3, 1, 3}. We repeat the experiments 25 times and report the average of the experimental results as well as the standard deviation. |