Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Truthful High Dimensional Sparse Linear Regression
Authors: Liyang Zhu, Amina Manseur, Meng Ding, Jinyan Liu, Jinhui Xu, Di Wang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 38,114 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,305 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper does not include experiments. |
| Researcher Affiliation | Academia | Liyang Zhu1, Amina Manseur1 , Meng Ding2, Jinyan Liu3 , Jinhui Xu2 , Di Wang1 1PRADA Lab, King Abdullah University of Science and Technology 2State University of New York at Buffalo 3Beijing Institute of Technology |
| Pseudocode | Yes | Algorithm 1 (ε, δ)-DP Algorithm for Sparse Linear Regression |
| Open Source Code | No | Our paper does not include experiments. |
| Open Datasets | No | Our paper does not include experiments. |
| Dataset Splits | No | Our paper does not include experiments. |
| Hardware Specification | No | Our paper does not include experiments. |
| Software Dependencies | No | Our paper does not include experiments. |
| Experiment Setup | No | Our paper does not include experiments. |