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..
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
Authors: Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama4122-4129
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results demonstrate the advantages of S-disc over the existing discrepancy measures. [...] We demonstrate the effectiveness of S-disc for unsupervised domain adaptation in experiments. |
| Researcher Affiliation | Collaboration | The University of Tokyo, Tokyo, Japan RIKEN, Tokyo, Japan |
| Pseudocode | Yes | Algorithm 1: S-disc Estimation for the 0-1 Loss |
| Open Source Code | No | The paper cites "ar Xiv:1809.03839" as a longer version of the paper, but does not provide a direct link to source code for the described methodology. It mentions using "scikit-learn" and "picos/cvxopt" as third-party tools but not its own implementation code. |
| Open Datasets | Yes | Here, we used MNIST (Le Cun, Cortes, and Burges 2010) dataset [...] Clean source domains: Five grayscale MNIST-M (Ganin et al. 2016), [...] Target domain: MNIST. |
| Dataset Splits | No | The paper specifies the total number of examples used for source and target domains (e.g., 'The number of examples was ranged from {1000, 2000, ..., 20000} for each domain'), but it does not provide explicit train/validation/test split percentages, absolute counts for each split, or references to predefined splits for reproduction. |
| Hardware Specification | Yes | The simulation was run on 2.8GHz Intel R Core i7. |
| Software Dependencies | No | The paper mentions using 'scikit-learn (Pedregosa et al. 2011)', 'picos' and 'cvxopt', but it does not specify version numbers for these software dependencies, which are crucial for reproducibility. |
| Experiment Setup | Yes | logistic regression implemented with scikit-learn with default parameters (Pedregosa et al. 2011). [...] The Gaussian noise with standard deviation ϵ = 30, 40, 50 were added and clipped to force the value to between 0 255. |