$\ell_1$-regression with Heavy-tailed Distributions
Authors: Lijun Zhang, Zhi-Hua Zhou
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper focuses on the statistical property of (6), and we leave the design of efficient optimization procedures as a future work. |
| Researcher Affiliation | Academia | Lijun Zhang, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {zhanglj, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | No | No. The paper primarily focuses on theoretical analysis and proofs, stating 'This paper focuses on the statistical property of (6), and we leave the design of efficient optimization procedures as a future work.' No pseudocode or algorithm blocks are provided. |
| Open Source Code | No | No. The paper states 'We will provide detailed investigations in an extended paper.' and 'we leave the design of efficient optimization procedures as a future work.' There is no concrete access to source code provided. |
| Open Datasets | No | No. The paper is theoretical and discusses 'input-output pairs that are independently drawn from an unknown distribution P' for its analysis, but it does not mention or provide access information for any specific public dataset. |
| Dataset Splits | No | No. As a theoretical paper without empirical experiments, it does not specify any dataset split information for training, validation, or testing. |
| Hardware Specification | No | No. As a theoretical paper, it does not describe any experimental hardware specifications. |
| Software Dependencies | No | No. As a theoretical paper, it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | No. The paper is theoretical and states, 'This paper focuses on the statistical property of (6), and we leave the design of efficient optimization procedures as a future work.' Consequently, no specific experimental setup details or hyperparameters are provided. |