Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fundamental Benefit of Alternating Updates in Minimax Optimization
Authors: Jaewook Lee, Hanseul Cho, Chulhee Yun
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Experiments and subsequent subsections describe empirical evaluation. For instance, We conducted experiments on a (3 + 3)-dimensional SCSC quadratic game to visually compare the convergence speed of the algorithms in Figure 1. and We run further experiments on (100 + 100)-dimensional SCSC quadratic games to extensively compare GDA, EG, OGD, and Alex-GDA. |
| Researcher Affiliation | Academia | 1KAIST AI, South Korea. |
| Pseudocode | Yes | Algorithm 1 Sim-GDA and Alt-GDA and Algorithm 2 Alternating-Extrapolation GDA (Alex-GDA). |
| Open Source Code | Yes | The experiments in Tables 1 and 2 can be reproduced with our code available at Git Hub.3 github.com/Hanseul Jo/Alex-GDA |
| Open Datasets | Yes | For MNIST (Deng, 2012)... For CIFAR-10 (Krizhevsky et al., 2009)... For LSUN-Bedroom 64 64 dataset (Yu et al., 2015)... |
| Dataset Splits | No | The paper describes using MNIST, CIFAR-10, and LSUN Bedroom datasets but does not explicitly provide details about train/validation/test splits, only mentioning training for GANs. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions using PyTorch and Adam optimizer but does not specify their version numbers or other software dependencies with version information. |
| Experiment Setup | Yes | Parameter tuning. We tuned step sizes and other parameters (like γ and δ of Alex-GDA) by grid search... We tune the momentum parameters mx, my { 0.99, 0.95, 0.9, 0.8, 0.7, ..., 0.9, 0.95, 0.99}. We tune γ and δ for Alex-GDA as γ, δ {0.5, 0.6, 0.7, ..., 3.0}. |