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..
A Fast and Provable Algorithm for Sparse Phase Retrieval
Authors: Jian-Feng CAI, Yu Long, Ruixue WEN, Jiaxi Ying
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that our algorithm achieves a significantly faster convergence rate than state-of-the-art methods. ... In this section, we present a series of numerical experiments designed to validate the efficiency and accuracy of our proposed algorithm. |
| Researcher Affiliation | Academia | Jian-Feng Cai1,2, Yu Long3 , Ruixue Wen1, Jiaxi Ying1,2 1 Hong Kong University of Science and Technology 2 HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute 3 Guangxi University |
| Pseudocode | Yes | Algorithm 1 Proposed algorithm |
| Open Source Code | Yes | Our codes are available at https://github.com/jxying/Sparse PR. |
| Open Datasets | No | The paper generates synthetic data for experiments, stating: "The true signal x has s nonzero entries, where the support is selected uniformly from all subsets of [n] with cardinality s, and their values are independently generated from the standard Gaussian distribution N(0, 1)." It does not use or provide access to any publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits. It describes generating synthetic data and averaging results over multiple independent trial runs (e.g., "average of 100 independent trial runs") but does not specify data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | All experiments were conducted on a 2 GHz Intel Core i5 processor with 16 GB of RAM, and all compared methods were implemented using MATLAB. |
| Software Dependencies | No | The paper states that "all compared methods were implemented using MATLAB." However, it does not specify a version number for MATLAB or any other software libraries or dependencies used, which is required for reproducibility. |
| Experiment Setup | Yes | We fine-tune the parameters and set: ฮฑ = 0.7 for Th WF; ฮณ = 0.5, ยต = 1 and |I| = m/6 for SPARTA; ฮท = 0.95 for both HTP and our algorithm. The maximum number of iterations for each algorithm is 1000. |