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 [1].
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds
Authors: Haixiang Zhang, Yingjie Bi, Javad Lavaei
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper considers the global geometry of general low-rank minimization problems via the Burer-Monteiro factorization approach. For the rank-1 case, we prove that there is no spurious second-order critical point... For the arbitrary rank-r case, the same property is established... We design a counterexample... we prove that all second-order critical points have a positive correlation to the ground truth. Finally, the strict saddle property... is established for both the symmetric and asymmetric problems... |
| Researcher Affiliation | Academia | Haixiang Zhang Department of Mathematics University of California, Berkeley Berkeley, CA 94704 EMAIL Yingjie Bi Department of IEOR University of California, Berkeley Berkeley, CA 94704 EMAIL Javad Lavaei Department of IEOR University of California, Berkeley Berkeley, CA 94704 EMAIL |
| Pseudocode | Yes | Algorithm 1 Singular Value Projection (SVP) Algorithm |
| 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 | This paper is theoretical and does not describe empirical experiments with datasets. It mentions applications like "matrix completion" and "phase retrieval" but does not use any specific dataset for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not describe empirical experiments with data splits, so no validation split information is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe empirical experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe empirical experiments; therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | This paper is theoretical and does not describe empirical experiments; therefore, no experimental setup details, hyperparameters, or training settings are provided. |