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..
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Authors: Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on the application of ensemble learning confirm that our Asy SZO+ has a faster convergence rate than the existing (asynchronous) stochastic zeroth-order algorithms. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA 2School of Electronic Engineering, Xidian University, Xi an, Shaanxi, China. |
| Pseudocode | Yes | Algorithm 1 Asynchronous Stochastic Zeroth-order Optimization (Asy SZO) and Algorithm 2 New Asynchronous Stochastic Zeroth-Order Algorithm with Variance Reduction and Mini-Batch (Asy SZO+) |
| Open Source Code | No | No explicit statement or link providing access to the source code for the described methodology was found. The paper only states 'We implement our Asy SZO+ in C++, where the shared memory parallel computation is handled via Open MP (Chandra, 2001). Similarly, we implement Asy SZO using C++ and Open MP.' |
| Open Datasets | Yes | As mentioned in (Dror et al., 2012), KDD-Cup 2011 challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings. ... We were able to obtain the predicted ratings of N = 237 individual models on the KDD-Cup Track 1 testing data set from the NTU KDD-Cup team, which is a matrix A with 6,005,940 rows (corresponding to the 6,005,940 samples in the testing data set) and 237 columns (corresponding to the outputs of 237 models on all samples in the testing set). |
| Dataset Splits | No | The paper mentions 'validation set' and its size ('l = 6, 005, 940 is the size of the validation set') and a 'testing data set', but does not provide specific details on how the dataset was split into training, validation, and testing portions (e.g., percentages or splitting methodology). |
| Hardware Specification | Yes | Our experiments are performed on a 32-core two-socket Intel Xeon E5-2699 machine where each socket has 16 cores. |
| Software Dependencies | No | The paper states 'We implement our Asy SZO+ in C++, where the shared memory parallel computation is handled via Open MP (Chandra, 2001),' but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper mentions 'mini-batch sizes 1 and 100' and that 'γ is set as a fixed constant' but does not provide the specific value of γ or other detailed hyperparameters like learning rates, number of epochs, or optimizer settings used in the experiments. |