Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Authors: Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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. |