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.
Efficient Discrepancy Testing for Learning with Distribution Shift
Authors: Gautam Chandrasekaran, Adam Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 47,696 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,061 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 have any experiments. |
| Researcher Affiliation | Academia | Gautam Chandrasekaran UT Austin Adam R. Klivans UT Austin Vasilis Kontonis UT Austin Konstantinos Stavropoulos UT Austin Arsen Vasilyan UC Berkeley |
| Pseudocode | Yes | Algorithm 1: Chow Matching Tester Algorithm 2: TDS learning through Chow matching |
| Open Source Code | No | Our paper does not include experiments requiring code. |
| Open Datasets | No | Our paper does not have any experiments. |
| Dataset Splits | No | Our paper does not have any experiments. |
| Hardware Specification | No | Our paper does not have any experiments. |
| Software Dependencies | No | Our paper does not have any experiments. |
| Experiment Setup | No | Our paper does not have any experiments. |