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 [1].
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Authors: Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world data sets demonstrate that our methods provide substantial utility gains for typical privacy requirements. |
| Researcher Affiliation | Academia | Bargav Jayaraman Department of Computer Science University of Virginia Charlottesville, VA 22903 EMAIL Lingxiao Wang Department of Computer Science University of California, Los Angeles Los Angeles, CA 90095 EMAIL David Evans Department of Computer Science University of Virginia Charlottesville, VA 22903 EMAIL Quanquan Gu Department of Computer Science University of California, Los Angeles Los Angeles, CA 90095 EMAIL |
| Pseudocode | No | The paper describes methods textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/bargavj/distributedMachine Learning.git |
| Open Datasets | Yes | For classification, we use a regularized logistic regression model over the KDDCup99 [25] data set (additional experiments on the Adult [2] data set yield similar results, described in Appendix B.3). ... For regression, we train a ridge regression model over the KDDCup98 [40] data set... |
| Dataset Splits | No | We randomly sample 70,000 records and divide it into training set of 50,000 records and test set of 20,000 records. (Only training and test sets are explicitly mentioned, not a separate validation set split.) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | For all the experiments, we set Lipschitz constant G = 1, learning rate η = 1, regularization coefficient λ = 0.001, privacy budget ϵ = 0.5, failure probability δ = 0.001 and total number of iterations T = 1, 500 for gradient descent. |