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..
Hypothesis Transfer Learning via Transformation Functions
Authors: Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world data demonstrate the effectiveness of our framework. |
| Researcher Affiliation | Academia | Simon S. Du Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Transformation Function based Transfer Learning |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use two datasets from the kin family in Delve [Rasmussen et al., 1996]. |
| Dataset Splits | Yes | Hyper-parameters were picked using grid search with 10-fold cross-validation on the target data (or source domain data when not using the target domain data). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components or libraries used. |
| Experiment Setup | Yes | We set nso to 320, and vary nta in {10, 20, 40, 80, 160, 320}. Hyper-parameters were picked using grid search with 10-fold cross-validation on the target data (or source domain data when not using the target domain data). |