Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Authors: Baojian Zhou, Yifan Sun
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results suggest that even these improved bounds are pessimistic, showing fast convergence in recovering real-world images with graph-structured sparsity. We empirically demonstrate that the proposed methods are more effective and efficient compared with PGD-based and MP-based methods on the graph-structured linear regression problem. We empirically evaluate our methods through the task of graph-structured linear regression problem over several graph-structured images. |
| Researcher Affiliation | Academia | 1School of Data Science, Fudan University, Shanghai, China 2Department of Computer Science, Stony Brook University, Stony Brook, New York, USA. |
| Pseudocode | Yes | Algorithm 1 FW-type methods for GSCOs |
| Open Source Code | Yes | Our code and datasets will be made publically available upon publication, and is included in the submission. Our code and datasets are accessible at https://github.com/baojian/dmo-fw. |
| Open Datasets | Yes | Our code and datasets will be made publically available upon publication, and is included in the submission. We use two datasets: 1) 10 MNIST images. ... 2) Angio image. We also choose a sparse angio image from (Hegde et al., 2015b). |
| Dataset Splits | No | The paper specifies number of samples (n = 2.5 | supp(x )| samples, n = 5 | supp(x )| samples) and number of trials (20 trials) for experiments, but does not explicitly describe train/validation/test splits, percentages, or cross-validation methodology. |
| Hardware Specification | No | We run all methods on a sever with 246GB memory and 80 cores. |
| Software Dependencies | Yes | All methods are implemented in Python-3.8. The graph projection operator is implemented in C++11. |
| Experiment Setup | Yes | The step size of both DMO-FW and DMO-Acc FW are set to ηt = 2/(t + 2) for all t 0. The DMO we used is the head projection of Hegde et al. (2015b). We use Option I for DMO-ACCFW and simply set L = 1. Specifically, we pick n = 2.5 | supp(x )| samples. We run each experiment for 20 trials, and compare our methods against the generalized MP (GEN-MP) discussed in Locatello et al. (2018). |