Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Regularized Wasserstein Means for Aligning Distributional Data
Authors: Liang Mi, Wen Zhang, Yalin Wang5166-5173
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,419 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,795 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 demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout. We evaluate our method on the office-31 dataset (Saenko and others 2010). Table 1: Classification Accuracy (%) on Office-31 W A |
| Researcher Affiliation | Academia | Liang Mi, Wen Zhang, Yalin Wang Arizona State University EMAIL |
| Pseudocode | Yes | Algorithm 1: Wasserstein Means Algorithm 2: Regularized Wasserstein Means |
| Open Source Code | Yes | Code is available at https://github.com/icemiliang/pyvot |
| Open Datasets | Yes | We evaluate our method on the office-31 dataset (Saenko and others 2010). |
| Dataset Splits | No | The paper describes data selection (e.g., 'randomly select 20 samples per class from Amazon and 10 samples per class from Webcam') but does not specify explicit train/validation/test splits or cross-validation setup. |
| Hardware Specification | Yes | CPU: Intel i5-7640x 4.0 GHz. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | The regularization weight of OTDA Laplacian is 0.3. It is from a search in {1, 0.3, 0.1, 0.03, 0.01}. The weight of RWM is 1 from a search in {3, 1, 0.3, 0.1, 0.03, 0.01} |