Private Stochastic Non-convex Optimization with Improved Utility Rates
Authors: Qiuchen Zhang, Jing Ma, Jian Lou, Li Xiong
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on both shallow and deep neural networks when respectively applied to simple and complex real datasets corroborate the theoretical results. |
| Researcher Affiliation | Academia | 1Emory University 2Xidian University {qiuchen.zhang, jing.ma, jian.lou, lxiong}@emory.edu, jlou@xidian.edu.cn |
| Pseudocode | Yes | Algorithm 1 DPage EM |
| Open Source Code | No | The paper mentions supplementary material for proofs and experiment details/results but does not explicitly state that source code for the methodology is provided. |
| Open Datasets | Yes | We conduct experiments on two real datasets: MNIST and CIFAR-10. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets but does not provide specific training/test/validation split percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Tensorflow and Py Torch but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | By setting π in eq.(4), ηk = ( 1 2)kη0, T k = (2)k T 0, tk Mon = (2)kt0 Mon, where η0 max{ θ(1 ϱ)2 2β , 1} and η0((T 0 t0 Mon) + 1 2(1 ϱ)t0 Mon) = 1 c4θµ for some 0 < c4 < 2 (which gives η0(T 0 t0 Mon + t0 Mon 1 2(1 ϱ)) > 1 2θµ), in Algorithm 1 |