Robust Testing in High-Dimensional Sparse Models
Authors: Anand Jerry George, Clément L Canonne
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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) anand.george@epfl.ch Clément L. Canonne School of Computer Science The University of Sydney clement.canonne@sydney.edu.au |
| 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. |