Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions
Authors: Yunwen Lei, Yiming Ying
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report some preliminary experiments to support our theory. We consider the dataset IJCNN available from the LIBSVM website (Chang & Lin, 2011) and report the average of experimental results from 25 repetitions. |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom 2Department of Computer Science, TU Kaiserslautern, Kaiserslautern 67653, Germany 3Department of Mathematics and Statistics, State University of New York at Albany, USA y.lei@bham.ac.uk yying@albany.edu |
| Pseudocode | Yes | The framework of stochastic variance-reduced optimization is described in Algorithm 1 in Appendix D.3. |
| Open Source Code | No | No statement or link providing concrete access to open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We consider the dataset IJCNN available from the LIBSVM website (Chang & Lin, 2011) |
| Dataset Splits | Yes | We use 80 percents of the dataset for training and reserve the remaining 20 percents for testing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instances) are mentioned for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | We apply SGD with the step size ηt = 1/(1 + 0.001t) and compute the testing error of {wt} on the testing dataset. |