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..
Transductive Learning is Compact
Authors: Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All our results are theoretical, and stated with their full set of required assumptions. |
| Researcher Affiliation | Academia | Julian Asilis USC EMAIL Siddartha Devic USC EMAIL Shaddin Dughmi USC EMAIL Vatsal Sharan USC EMAIL Shang-Hua Teng USC EMAIL |
| Pseudocode | No | The paper contains theoretical proofs and theorems but no pseudocode or algorithm blocks are explicitly presented. |
| Open Source Code | No | The paper does not include any experiments requiring code. (NeurIPS Paper Checklist) |
| Open Datasets | No | The paper does not include any experiments. (NeurIPS Paper Checklist) |
| Dataset Splits | No | The paper does not include any experiments. (NeurIPS Paper Checklist) |
| Hardware Specification | No | The paper does not include any experiments. (NeurIPS Paper Checklist) |
| Software Dependencies | No | The paper does not include any experiments. (NeurIPS Paper Checklist) |
| Experiment Setup | No | The paper does not include any experiments. (NeurIPS Paper Checklist) |