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..
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Authors: Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on both synthetic and real data verify that our model can produce interpretable domain indices which enable us to achieve superior performance compared to state-of-the-art domain adaptation methods. In this section, we compare VDI with existing DA methods on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1Rutgers University, 2Hong Kong University of Science and Technology, 3Massachusetts Institute of Technology |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Code is available at https://github.com/Wang-ML-Lab/VDI. |
| Open Datasets | Yes | Circle (Wang et al., 2020) is a synthetic dataset with 30 domains for binary classification. DG-15 and DG-16 (Xu et al., 2022). TPT-48 (Xu et al., 2022) is a real-world regression dataset... Comp Cars (Yang et al., 2015) is a car image dataset... |
| Dataset Splits | No | The paper mentions using source and target domains but does not explicitly state train/validation/test dataset splits with specific percentages or counts needed for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | For experiments on all 4 datasets, we set the dimension of global domain indices to 2. For Circle, DG-15, DG-60, the dimension of local domain indices is 4, while for TPT-48 and Comp Cars, the dimension of local domain indices is 8. Our model is trained with 20 to 70 warmup steps, learning rates ranging from 1 10 5 to 1 10 4, and λd ranging from 0.1 to 1. |