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..
Rate-Optimal Subspace Estimation on Random Graphs
Authors: Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on the algorithms appear in Section 4. In this section, each experiment will repeat 100 times. In each iteration, the randomization procedure follows these steps: |
| Researcher Affiliation | Collaboration | 1Department of Management Sciences, City University of Hong Kong 2Cognitive Computing Lab, Baidu Research 3Department of Statistics, Rutgers University |
| Pseudocode | Yes | Algorithm 1 Hard Singular Value Thresholding Algorithm 2 Soft Singular Value Thresholding Algorithm 3 Singular space estimation |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The numerical experiments in Section 4 describe a procedure to 'Randomly generate matrices' and 'Generate the adjacency matrix of the random bipartite graph with connectivity matrix M', indicating the use of synthetically generated data rather than a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation setup where data is randomly generated for each experiment iteration (e.g., 'Randomly generate matrices M1...', 'Generate the adjacency matrix...'). It does not specify train, validation, or test splits of a fixed dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | We consider the following parameters in Θ1(n1, n2, r, p). n1 = n2 = 1000, r = 3, p = 0.01, 0.03, 0.05. In the following experiments, we vary the regularization constant c from 0.2 to 1, where the default constant equals to 2 in Algorithm 1. |