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 Nonconvex Optimization Framework for Low Rank Matrix Estimation
Authors: Tuo Zhao, Zhaoran Wang, Han Liu
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments for matrix sensing to support our theoretical analysis. We choose m = 30, n = 40, and k = 5, and vary d from 300 to 900. Each entry of Ai s are independent sampled from N(0, 1). We then generate M = UV >, where e U 2 Rm k and e V 2 Rn k are two matrices with all their entries independently sampled from N(0, 1/k). We then generate d measurements by bi = h Ai, Mi for i = 1, ..., d. Figure 1 illustrates the empirical performance of the alternating exact minimization and alternating gradient descent algorithms for a single realization. |
| Researcher Affiliation | Academia | Tuo Zhao Johns Hopkins University Zhaoran Wang Han Liu Princeton University |
| Pseudocode | Yes | Algorithm 1 A family of nonconvex optimization algorithms for matrix sensing. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('Each entry of Ai s are independent sampled from N(0, 1). We then generate M = UV >'), but it does not use or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating data for its experiments but does not specify any training, validation, or test dataset splits, percentages, or a methodology for creating such splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | The step size for the alternating gradient descent algorithm is determined by the backtracking line search procedure. |