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.
Ratio Trace Formulation of Wasserstein Discriminant Analysis
Authors: Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,458 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,931 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 | Numerical experiments on real datasets show promising results of the ratio trace formulation of WDA in both classification and clustering tasks. |
| Researcher Affiliation | Industry | Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li Cognitive Computing Lab Baidu Research No.10 Xibeiwang East Road, Beijing 100193, China 10900 NE 8th St. Bellevue, Washington 98004, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 WDA-eig algorithm Algorithm 2 Iterative WDA clustering |
| Open Source Code | No | The paper does not contain an explicit statement that the authors' code for the described methodology is open-source, nor does it provide a direct link to a code repository for their implementation. |
| Open Datasets | Yes | We extract 1000 samples in the MNIST dataset as the training set and use 10000 samples in the test set. We use four real world datasets to evaluate the proposed method: the MNIST dataset for digits recognition, the 15-scene dataset [19] for multi-class image recognition, the KTH action recognition database [33] for multi-class video recognition, and the 20 newsgroup dataset for text classification. |
| Dataset Splits | No | The paper specifies a training and test set split for MNIST but does not explicitly mention a separate validation set or its split percentage for any dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only mentions experiments were run and evaluated. |
| Software Dependencies | No | The paper mentions software like "pymanopt solvers", "Sinkhorn’s fixed-point iterations", "Greenkhorn algorithm", "APDAMD", "K-Nearest-Neighbors classifier (KNN)", "K-means", "FDA", "PCA", "LFDA" but does not specify version numbers for these components, which would be necessary for reproducible software dependencies. |
| Experiment Setup | Yes | For each λ, we run each algorithms for 100 randomly-initialized trials, and the results are shown in Table 1. In implementation of FDA/WDA-eig we add a small perturbation term ϵIp on Cw to make the denominator positive definite, and we choose ϵ = 2 in this setting... The Wasserstein regularizer λ is coarsely tuned, where we choose λ = 0.01 for MNIST and 15-scene, λ = 10 for KTH, and λ = 5 for 20ng. For (3) and (4) we use the subspace obtained by PCA as initialization and p = Nc 1 as the subspace dimensions. No regularization term is added to Cw. |