Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
Authors: Luo Luo, Yujun Li, Cheng Chen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct empirical studies for our methods against the classical GDA algorithm [20] on both synthetic problem and real-world application. |
| Researcher Affiliation | Collaboration | Luo Luo School of Data Science Fudan University luoluo@fudan.edu.cn Yujun Li Noah s Ark Lab Huawei Technologies Co., Ltd. liyujun9@huawei.com Cheng Chen School of Physical and Mathematical Sciences Nanyang Technological University cheng.chen@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1 AGD(h, y0, K, , ), Algorithm 2 Minimax Cubic-Newton (MCN), Algorithm 3 Inexact Minimax Cubic-Newton, Algorithm 4 Cubic-Solver, Algorithm 5 Final-Cubic-Solver |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | We compare IMCN with GDA on the domain adaptation problem between two different datasets: MNIST [19] and MNIST-m [9]. |
| Dataset Splits | No | The paper mentions using MNIST and MNIST-m datasets but does not explicitly state the training, validation, or test splits within the provided text. |
| Hardware Specification | No | The paper states in its checklist that it includes the type of resources used, but these specific details (e.g., GPU/CPU models) are not present in the provided main paper text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers in the provided text. |
| Experiment Setup | Yes | The learning rate of GDA and AGD step in MCN is selected from - c 10 i : c 2 {1, 5}, i 2 {1, 2, 3}. For MCN method, we choose M = 10. |