Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
Authors: Luo Luo, Yujun Li, Cheng Chen
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,637 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,485 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Yujun Li Noah s Ark Lab Huawei Technologies Co., Ltd. EMAIL Cheng Chen School of Physical and Mathematical Sciences Nanyang Technological University EMAIL |
| 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. |