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..
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Authors: Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The entire paper is a theoretical work, presenting definitions, theorems, proofs, and an algorithm (Algorithm 1) for a mathematical problem. There are no sections discussing empirical studies, datasets, performance metrics, or experimental results. For example, "Theorem 3.1", "Definition 2.1", "Algorithm 1" are prevalent throughout the document. |
| Researcher Affiliation | Academia | Xiaowei Zhang, Hongwei Liu, Guohua Liu, Lei Yang, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, 100876. |
| Pseudocode | Yes | Algorithm 1: Subgradient Method for R-ORMR |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of source code for the described methodology. |
| Open Datasets | No | This theoretical paper does not involve experiments with datasets, and thus provides no information regarding publicly available or open datasets. |
| Dataset Splits | No | This theoretical paper does not involve experiments with datasets, and thus provides no information regarding dataset splits. |
| Hardware Specification | No | The paper does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper, being purely theoretical, does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical work and does not detail an experimental setup with hyperparameters or system-level training settings. |