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..
H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
Authors: Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the performance of our algorithm for a classification task using the logistic regression model on the Spambase dataset. |
| Researcher Affiliation | Academia | Guanqiang Zhou George Mason University EMAIL Ping Xu University of Texas Rio Grande Valley EMAIL Yue Wang Georgia State University EMAIL Zhi Tian George Mason University EMAIL |
| Pseudocode | Yes | Algorithm 1 Norm-Based Screening, Algorithm 2 H-nobs: Fair & Byzantine-Robust Distributed Gradient Descent |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for its proposed methodology or a link to a code repository. |
| Open Datasets | Yes | using the logistic regression model on the Spambase dataset [21]. We assign 2/3 of the 4601 total samples for training and the other 1/3 for testing. The results for these two datasets are presented in Appendix E. Table 3: The performance of H-nobs on the Law School dataset (with no attack). Table 4: The performance of H-nobs on the Credit Card Client dataset (with no attack). |
| Dataset Splits | No | We assign 2/3 of the 4601 total samples for training and the other 1/3 for testing. The paper explicitly mentions training and testing splits, but does not specify a separate validation split or its size/proportion. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions using a 'logistic regression model' but does not specify any software libraries or their version numbers, such as Python, PyTorch, TensorFlow, or scikit-learn. |
| Experiment Setup | Yes | In Figure 1, we plot the performance curves of model accuracy for the five considered aggregation measures... using learning rate η = 1 and number of iterations T = 300. In Table 1, we document both the model accuracy and the variance of local accuracies (the metric of model fairness) using different values of q and screening percentage β. The learning rate η and number of iterations T for each q value are carefully selected to ensure fast and stable convergence. Finally, we compare H-nobs... using learning rate η = 0.5 and number of iterations T = 1000. |