signSGD via Zeroth-Order Oracle
Authors: Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations on image classification datasets MNIST and CIFAR-10 demonstrate the superior performance of ZO-sign SGD on the generation of adversarial examples from black-box neural networks. |
| Researcher Affiliation | Collaboration | Sijia Liu Pin-Yu Chen Xiangyi Chen Mingyi Hong MIT-IBM Watson AI Lab, IBM Research University of Minnesota, Twin Cities |
| Pseudocode | Yes | Algorithm 1 Generic sign-based gradient descent |
| Open Source Code | No | The paper does not include an unambiguous statement of releasing its own source code or a direct link to a repository for the work described. |
| Open Datasets | Yes | Our empirical evaluations on image classification datasets MNIST and CIFAR-10 |
| Dataset Splits | No | The paper mentions training samples and testing samples (n = 2000 training, 200 testing) for the synthetic dataset, but does not explicitly specify a validation set or general data split percentages for reproduction across all datasets. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or cloud computing instances with specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers (e.g., 'Python 3.8, PyTorch 1.9, and CUDA 11.1'). |
| Experiment Setup | Yes | We find the best constant learning rate for algorithms via a greedy search over η [0.001, 0.1] (see Appendix 8.1 for more details), and we choose the smoothing parameter µ = 10/ Td. Unless specified otherwise, let b = q = 10, T = 5000 and d = 100. In our experiment, we set c = 1 for MNIST and c = 0.1 for CIFAR-10. We also set the same parameters for each method, i.e., µ = 0.01, q = 9, and δ = 0.05 for MNIST and δ = 0.0005 for CIFAR-10, to accommodate to the dimension factor d. |