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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |