Multi-Source Domain Adaptation: A Causal View
Authors: Kun Zhang, Mingming Gong, Bernhard Schoelkopf
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world data verify our theoretical results. and 3 Experiments We first test the performance of the multi-source DA methods proposed in Section 2.3 for classification on simulated data. |
| Researcher Affiliation | Academia | Kun Zhang MPI for Intelligent Systems 72076, T ubingen, Germany kzhang@tuebingen.mpg.de Mingming Gong QCIS, University of Technology Sydney Ultimo, NSW 2007, Australia gongmingnju@gmail.com Bernhard Sch olkopf MPI for Intelligent Systems 72076, T ubingen, Germany bs@tuebingen.mpg.de |
| Pseudocode | No | The paper describes the methods textually and mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The sentiment data (Blitzer, Dredze, and Pereira 2007) consist of review text and labels for four categories of goods (domains): book, dvd, electronics, and kitchen; each domain contains 2000 data points (or reviews) with four labels (or classes). We also compared our approaches with alternatives on the object recognition data (Griffin, Holub, and Perona 2007), as done by (Gong et al. 2012). |
| Dataset Splits | Yes | the SVM parameters were selected by 5-fold cross validation on the parameter grids. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, or cloud computing instances with their specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'SVM' and 'Gauss kernel' but does not specify any software names with version numbers for the implementation. |
| Experiment Setup | Yes | In our methods, we simply set the kernel width to 0.5, and the SVM parameters were selected by 5-fold cross validation on the parameter grids. and We used SVM for all the DA methods, and the SVM hyper parameters were selected by 5-fold cross validation on a grid. |