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..
Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation
Authors: Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan, Qingyao Wu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Software Engineering, South China University of Technology, China 2Computer Vision Laboratory, ETH Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 summarizes the main steps of the proposed SGW algorithm. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The synthetic data are generated from Wine dataset 1, which includes 178 samples with three classes. 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ Object Recognition: We use Office and Caltech-256 datasets for the object recognition task. Office [Saenko et al., 2010]... Caltech-256 (C) [Griffin et al., 2007]... Text Classification: The text classification task is conducted on the Reuters multilingual dataset [Amini et al., 2009] |
| Dataset Splits | No | The paper describes how training and test data are used but does not explicitly define a separate validation set or split for model tuning. |
| Hardware Specification | Yes | The experiments are performed on a workstation with Xeon 3.40 GHz CPU and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions using a classifier (SVM), but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | For simplicity and fair comparison, we set the trade-off parameter of SVM to C = 1 for all the methods and tasks. The parameters of SGW are empirically set to ϵ = 0.01, λ = 1 and γ = 1, and the sensitivity study is provided in Section 5.6. |