Linear regression without correspondence
Authors: Daniel J. Hsu, Kevin Shi, Xiaorui Sun
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 djhsu@cs.columbia.edu Kevin Shi Columbia University New York, NY kshi@cs.columbia.edu Xiaorui Sun Microsoft Research Redmond, WA xiaoruisun@cs.columbia.edu |
| 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. |