On Hypothesis Transfer Learning of Functional Linear Models
Authors: Haotian Lin, Matthew Reimherr
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 <hzl435@psu.edu>. |
| 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. |