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..
$\ell_1$-regression with Heavy-tailed Distributions
Authors: Lijun Zhang, Zhi-Hua Zhou
NeurIPS 2018 | Venue PDF | 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 EMAIL |
| 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. |