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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
On Hypothesis Transfer Learning of Functional Linear Models
Authors: Haotian Lin, Matthew Reimherr
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 39,056 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,849 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed algorithms is demonstrated via extensive synthetic data as well as real-world data applications. |
| Researcher Affiliation | Academia | 1Department of Statistics, The Pennsylvania State University, University Park, PA, USA. Correspondence to: Haotian Lin <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 TL-FLR |
| Open Source Code | Yes | 1The R code and the application datasets are available in https://github.com/haotianlin/HTL-FLM. |
| Open Datasets | Yes | We consider the Human Activity Recognition (HAR) dataset (Anguita et al., 2013) |
| Dataset Splits | Yes | We randomly split the target sector into the train (80%) and test (20%) set and report the ratio of the four approaches prediction errors to OFLR s on the test set. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware used for running the experiments (e.g., GPU models, CPU types, or cloud computing instance details). |
| Software Dependencies | No | The paper mentions 'The R code' in a footnote, indicating the programming language used, but it does not specify any particular software libraries, packages, or solvers with their version numbers. |
| Experiment Setup | Yes | For each algorithm, we set the regularization parameters as λ1 and λ2 as the optimal values in Theorem 4.3 and select the constants using crossvalidation. |