Transductive Learning is Compact
Authors: Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 asilis@usc.edu Siddartha Devic USC devic@usc.edu Shaddin Dughmi USC shaddin@usc.edu Vatsal Sharan USC vsharan@usc.edu Shang-Hua Teng USC shanghua@usc.edu |
| 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) |