Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Authors: Hongchang Gao, Heng Huang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, the empirical studies on benchmark datasets validate our theoretical results. 4. Experiments |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA 2Department of Computer and Information Sciences, Temple University, Philadelphia, USA 3JD Finance America Corporation. |
| Pseudocode | Yes | Algorithm 1 Faster Zeroth-Order Frank-Wolfe Method (FZFW) ... Algorithm 2 Faster Zeroth-Order Conditional Gradient Method (FZCGS) ... Algorithm 3 u+ = condg(g, u, γ, ) (Qu et al., 2017) ... Algorithm 4 Faster First-Order Conditional Gradient Sliding Method (FCGS) |
| Open Source Code | No | The paper includes a footnote linking to a GitHub repository (https://github.com/IBM/ ZOSVRG-Black Box-Adv) for a pre-trained DNN model used in one experiment, but it does not state that the source code for the proposed methods is available. |
| Open Datasets | Yes | The dataset used in this experiment is Movie Lens100k... Following (Liu et al., 2018; Ji et al., 2019), we use the same pretrained DNN2 for MNIST dataset as the black-box model. |
| Dataset Splits | No | The paper describes synthetic data generation and mentions MovieLens100k and MNIST datasets, but it does not provide specific details on train/validation/test splits (e.g., percentages or sample counts) for any of these datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that would be needed to reproduce the experiments. |
| Experiment Setup | Yes | The hyperparameter s is set to 0.1. |