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..
Robust Testing in High-Dimensional Sparse Models
Authors: Anand Jerry George, Clément L Canonne
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work focuses on theoretical aspects of statistical estimation and high-dimensional testing |
| Researcher Affiliation | Academia | Anand Jerry George School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne (EPFL) EMAIL Clément L. Canonne School of Computer Science The University of Sydney EMAIL |
| Pseudocode | No | The paper describes theoretical methods and proofs, but no pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper focuses on theoretical contributions and does not mention releasing any source code. The checklist also indicates N/A for code related questions. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or dataset training. The checklist states N/A for data related questions. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies or dataset splits. The checklist states N/A for data related questions. |
| Hardware Specification | No | The paper is purely theoretical and does not involve running experiments that would require hardware specification. The checklist indicates N/A for compute related questions. |
| Software Dependencies | No | The paper is purely theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |