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].
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
Authors: Surbhi Goel, Abhishek Shetty, 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 | Surbhi Goel Department of Computer Science University of Pennsylvania EMAIL Abhishek Shetty Department of EECS UC Berkeley EMAIL Konstantinos Stavropoulos Department of Computer Science UT Austin EMAIL Arsen Vasilyan Department of EECS UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1: Outlier Removal Procedure |
| Open Source Code | No | Our paper does not have any 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 requiring code. |
| Experiment Setup | No | Our paper does not have any experiments. |