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].
Linear-Sample Learning of Low-Rank Distributions
Authors: Ayush Jain, Alon Orlitsky
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a polynomial-time algorithm curated SVD that returns an estimate M cur := M cur(X) that essentially achieves the above lower bound for all five models and all matrices M. The proofs establish new results on the rapid convergence of the spectral distance between the model and observation matrices, and may be of independent interest. |
| Researcher Affiliation | Academia | Ayush Jain and Alon Orlitsky Dept. of Electrical and Computer Engineering University of California, San Diego EMAIL |
| Pseudocode | Yes | The pseudo-code of the algorithm is in Appendix B. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not perform empirical evaluations on a dataset, therefore no public dataset is specified. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical evaluations on a dataset, therefore no training/validation/test splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details or hyperparameters. |