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..
Domain Generalization via Heckman-type Selection Models
Authors: Hyungu Kahng, Hyungrok Do, Judy Zhong
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 21,225 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,550 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 | We also demonstrate its efficacy empirically through simulations and experiments on a set of benchmark datasets comparing with other well-known DG methods. |
| Researcher Affiliation | Academia | 1Korea University 2NYU School of Medicine EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Two-Step Optimization for Heckman DG |
| Open Source Code | Yes | code available: https://github.com/hgkahng/domain-generalization-lightning |
| Open Datasets | Yes | To further demonstrate the effectiveness of Heckman DG on high-dimensional data regimes, we conducted experiments on four datasets from the WILDS benchmark (Koh et al., 2021): 1) CAMELYON17, 2) POVERTYMAP, 3) IWILDCAM, and 4) RXRX1. |
| Dataset Splits | Yes | Detailed descriptions of dataset statistics are presented in Table 5 of Appendix A.4. In the Domain row, the three numbers in parentheses denote the number of train, validation, and test domains. (e.g., CAMELYON17: 5 Hospitals (3, 1, 1)) |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU models, CPU models, or detailed specifications of computing resources. |
| Software Dependencies | No | The paper mentions software components like 'Dense Net-121', 'Res Net-18-MS', 'Res Net-50', 'Adam', and 'SGD' but does not specify version numbers for these or other key software dependencies (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | Details on training configurations of Heckman DG are provided in Table 6. This includes parameters such as 'Epochs', 'Batch Size', 'Learning Rate', 'Weight Decay', 'Image Net Weights', and 'Data Augmentation'. |