Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Authors: Hongchang Gao, Heng Huang
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,833 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,293 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |