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..
Lock-Free Optimization for Non-Convex Problems
Authors: Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results also show that both Hogwild! and Asy SVRG are convergent on non-convex problems, which successfully verifies our theoretical results. Experiment To verify our theoretical results about Hogwild! and Asy SVRG, we use a fully-connected neural network to construct a non-convex function. ... We use two datasets: connect-4 and MNIST4 to do experiments... |
| Researcher Affiliation | Academia | Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li National Key Laboratory for Novel Software Technology Department of Computer Science and Technology, Nanjing University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Hogwild! and Algorithm 2 Asy SVRG are explicitly presented. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology described in this paper is publicly available. |
| Open Datasets | Yes | We use two datasets: connect-4 and MNIST4 to do experiments and λ = 10 3. ... 4https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits for training/validation/test, or detailed splitting methodology). |
| Hardware Specification | Yes | The experiments are conducted on a server with 12 Intel cores and 64G memory. One possible reason is that we have two CPUs in our server, with 6 cores for each CPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We initialize w by randomly sampling from a Gaussian distribution with mean being 0 and variance being 0.01, and initialize b = 0. During training, we use a fixed stepsize for both Hogwild! and Asy SVRG. The stepsize is chosen from {0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001}, and the best is reported. For the iteration number of the inner-loop of Asy SVRG, we set M = n/p, where p is the number of threads. |