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.
Distribution-free inference for regression: discrete, continuous, and in between
Authors: Yonghoon Lee, Rina Barber
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,155 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,174 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 main theoretical results show that there are two regimes. |
| Researcher Affiliation | Academia | Yonghoon Lee Department of Statistics University of Chicago Chicago, IL 60637 EMAIL, Rina Foygel Barber Department of Statistics University of Chicago Chicago, IL 60637 EMAIL |
| Pseudocode | No | The paper describes the steps of its construction in Section 3, such as 'Step 1: estimate the effective support size', 'Step 2: estimate error at each repeated X value', and 'Step 3: construct the confidence interval'. While these are algorithmic steps, they are presented in prose with mathematical formulas rather than a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets, therefore it does not mention public access information for training data. The checklist explicitly states 'N/A' for experimental questions. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments with data splits. The checklist explicitly states 'N/A' for experimental questions. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring specific hardware. The checklist under 'If you ran experiments...' explicitly states '[N/A]' for questions related to compute resources. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. The checklist under 'If you ran experiments...' explicitly states '[N/A]' for related questions. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup with specific hyperparameters or training configurations. The checklist under 'If you ran experiments...' explicitly states '[N/A]' for related questions. |