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..
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations
Authors: Yao Shu, Jiongfeng Fang, Ying He, Fei Yu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by Opt Ex in practice. |
| Researcher Affiliation | Academia | Yao Shu# , Jiongfeng Fang , Ying Tiffany He , Fei Richard Yu Guangdong Lab of AI and Digital Economy (SZ), China College of Computer Science and Software Engineering, Shenzhen University, China School of Information Technology, Carleton University, Canada |
| Pseudocode | Yes | Algorithm 1: Opt Ex |
| Open Source Code | Yes | Our implementation is available at https://github.com/youyve/Opt Ex. |
| Open Datasets | Yes | Open AI Gym suite [38], CIFAR-10 [41], MNIST [45], Fashion MNIST [46], Shakespeare Corpus, Harry Potter and the Sorcerers Stone |
| Dataset Splits | No | The paper mentions using standard datasets like CIFAR-10 and MNIST, but does not explicitly state the training/validation/test splits used for these datasets. |
| Hardware Specification | Yes | The wallclock time is evaluated on a single NVIDIA RTX 4090 GPU. The wallclock time is evaluated on an AMD EPYC 7763 CPU. |
| Software Dependencies | No | The paper mentions using Deep Q-Network (DQN), Open AI Gym, and jax.vmap, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | all the baselines...are based on Adam [4] with a learning rate of 0.1, β1 = 0.9, and β2 = 0.999. Hyperparameters, including a learning rate of 0.001, a reward discount factor of 0.95, and a batch size of 256, are applied. based on SGD [1] with a learning rate of 0.001, a batch size of 512. |