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..
Private Stochastic Non-convex Optimization with Improved Utility Rates
Authors: Qiuchen Zhang, Jing Ma, Jian Lou, Li Xiong
IJCAI 2021 | Venue PDF | 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 EMAIL, EMAIL |
| 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 |