Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning
Authors: Zhuang Yang
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical tests using various benchmark data sets indicate the efficiency and robustness of our proposed algorithms. |
| Researcher Affiliation | Academia | Zhuang Yang EMAIL School of Computer Science and Technology Soochow University Suzhou, 215006, China |
| Pseudocode | Yes | Algorithm 1 PB-SVRGE Algorithm 2 PB-SVRGE-RSBB Algorithm 3 PB-SGD-RSBB |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In order to evaluate the proposed algorithms, we conducted the experiments on six standard data sets, where the details of these data sets were listed in Table 1. 1 Specifically, the data sets (a8a, covtype, ijcnn1 and news20) can be downloaded from LIBSVM (Chang and Lin, 2011). In addition, we took the non-convex logistic regression as the loss function. ... 1. For the CIFAR-10 data set, it can be downloaded from http://www.cs.toronto.edu/~kriz/cifar.html. For the MNIST data set, it can be accessed from http://yann.lecun.com/exdb/mnist/. |
| Dataset Splits | No | The paper mentions using several datasets (a8a, covtype, CIFAR-10, ijcnn1, MNIST, news20.binary) but does not provide specific details on how these datasets were split into training, validation, or test sets. For example, it only lists the total number of examples for each dataset in Table 1. |
| Hardware Specification | Yes | Note that all experiments were conducted on an Intel(R) Core(TM) i7-10750H CPU @2.60GHz 2.59GHz with MATLAB 2019a. |
| Software Dependencies | Yes | Note that all experiments were conducted on an Intel(R) Core(TM) i7-10750H CPU @2.60GHz 2.59GHz with MATLAB 2019a. |
| Experiment Setup | Yes | We studied the numerical behaviors of the proposed algorithms with the regularizer coefficient λ = 10^-1. ... On all data sets, we set b = 10. In addition, on a8a and i jcnn1, we set η = 0.01. While on covtype and news20.binary, we set η = 0.1. ... we set b = 10, b H = 20, ζ = 1 and γ = 0.9 on different data sets when executing PB-SVRGE-RSBB (Algorithm 2). The parameter ζ was chosen from {0.01, 0.1, 1, 10}. |