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 [1].
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
| 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. |