Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Authors: Baojian Zhou, Yifan Sun
ICML 2022 | Venue PDF | 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). |