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..
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Authors: Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several benchmark datasets show the effectiveness of our algorithm and also confirm our theory. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA 2Tencent AI Lab, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 Co DA; Algorithm 2 DSG |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We do experiments on 3 datasets: Cifar10, Cifar100 and Image Net. |
| Dataset Splits | No | The paper specifies training and testing data splits, but does not explicitly mention a separate validation set or split percentages for one. |
| Hardware Specification | No | The paper mentions 'a cluster of 4 computing nodes with each computer node having 4 GPUs', and states 'one machine corresponds to one GPU'. However, it does not specify the models of the GPUs (e.g., NVIDIA A100, Tesla V100) or any CPU details. |
| Software Dependencies | No | All algorithms are implemented by Py Torch (Paszke et al., 2019). The paper mentions PyTorch but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | For all algorithms, we set Ts = T03k, ηs = η0/3k. T0 and η0 are tuned for PPD-SG and set to the same for all other algorithms for fair comparison. T0 is tuned in [2000, 5000, 10000], and η0 is tuned in [0.1, 0.01, 0.001]. We fix the batch size for each GPU as 32. |