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..
Linear regression without correspondence
Authors: Daniel J. Hsu, Kevin Shi, Xiaorui Sun
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithms are not meant for practical deployment, but instead are intended to shed light on the computational difficulty of the least squares problem and the average-case recovery problem. |
| Researcher Affiliation | Collaboration | Daniel Hsu Columbia University New York, NY EMAIL Kevin Shi Columbia University New York, NY EMAIL Xiaorui Sun Microsoft Research Redmond, WA EMAIL |
| Pseudocode | Yes | Algorithm 1 Approximation algorithm for least squares problem |
| Open Source Code | No | The paper states: 'Our algorithms are not meant for practical deployment, but instead are intended to shed light on the computational difficulty of the least squares problem and the average-case recovery problem.' There is no mention or link to open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical models using i.i.d. draws from distributions (e.g., N(0, Id)) rather than using or referring to specific named public datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore it does not provide details on dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and discusses algorithmic properties and statistical bounds, without reporting empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithm design. It does not describe empirical implementations or list any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and statistical bounds. It does not detail an experimental setup with specific hyperparameter values or training configurations for empirical evaluation. |