A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression
Authors: Shusen Wang5305-5312
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Since extensive empirical studies of the DC method have been conducted by (Zhang, Duchi, and Wainwright 2013; Wang, Gittens, and Mahoney 2018), we focus on the theory without conducting experiments. |
| Researcher Affiliation | Academia | Shusen Wang Department of Computer Science, Stevens Institute of Technology shusen.wang@stevens.edu |
| Pseudocode | No | The paper focuses on theoretical analysis and proofs and does not include any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The authors state, 'we focus on the theory without conducting experiments,' implying that no new code for the methodology described in this paper was developed or released. |
| Open Datasets | No | The paper states, 'we focus on the theory without conducting experiments,' indicating that no empirical evaluations were performed using a dataset. |
| Dataset Splits | No | The paper is theoretical and states, 'we focus on the theory without conducting experiments,' so no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | The paper states, 'we focus on the theory without conducting experiments,' and thus does not provide any hardware specifications for experimental runs. |
| Software Dependencies | No | The paper explicitly states, 'we focus on the theory without conducting experiments,' and consequently does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper explicitly states, 'we focus on the theory without conducting experiments,' and therefore does not describe any experimental setup details such as hyperparameters or training configurations. |