A kernel test for quasi-independence
Authors: Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we give a detailed empirical evaluation of our method. We begin with challenging synthetic datasets exhibiting periodic quasi-dependence, as would be expected for example from seasonal or daily variations, where our approach strongly outperforms the alternatives. Additionally, we show our test is consistently the best test in data-scenarios in which the censoring percentage is relatively high, see Figure 6. Next, we apply our test statistic to three real-data scenarios, shown in Figure 1... |
| Researcher Affiliation | Academia | Tamara Fernández Gatsby Unit University College London t.a.fernandez@ucl.ac.uk Wenkai Xu Gatsby Unit University College London xwk4813@gmail.com Marc Ditzhaus Department of Statistics TU Dortmund University marc.ditzhaus@tu-dortmund.de Arthur Gretton Gatsby Unit University College London arthur.gretton@gmail.com |
| Pseudocode | No | The paper describes methods and procedures mathematically and in prose, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We apply our test statistic to three real-data scenarios, shown in Figure 1: a survival analysis study for residents in the Channing House retirement community in Palo Alto, California [18]; a study of transfusion-related AIDS [24]; and a spontaneous abortion study [26]. |
| Dataset Splits | No | The paper mentions sample sizes (e.g., 'n = 100 on the left; n = 200 on the right') and censoring rates (e.g., 'censoring rate: 50%') for its synthetic experiments and sizes for real datasets, but does not provide specific training/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions the use of 'Gaussian' and 'IMQ' kernels and references methods for bandwidth optimization, but it does not specify any software dependencies with version numbers (e.g., Python version, library versions like TensorFlow or PyTorch). |
| Experiment Setup | No | The paper mentions general settings like 'wild-bootstrap size...set to be 500' and 'α = 0.05', along with kernel choices. However, it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or detailed training configurations for the experiments. |