A Linear-Time Kernel Goodness-of-Fit Test
Authors: Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, the performance of our method exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test. In experiments (Section 5), our new linear-time test is able to detect subtle local differences between the density p(x), and the unknown q(x) as observed through samples. |
| Researcher Affiliation | Academia | Wittawat Jitkrittum Gatsby Unit, UCL wittawatj@gmail.com Wenkai Xu Gatsby Unit, UCL wenkaix@gatsby.ucl.ac.uk Zoltán Szabó CMAP, École Polytechnique zoltan.szabo@polytechnique.edu Kenji Fukumizu The Institute of Statistical Mathematics fukumizu@ism.ac.jp Arthur Gretton Gatsby Unit, UCL arthur.gretton@gmail.com |
| Pseudocode | No | The paper describes algorithms and procedures in prose and mathematical formulas, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/wittawatj/kernel-gof. |
| Open Datasets | Yes | We consider crime data from the Chicago Police Department, recording n = 11957 locations (latitude-longitude coordinates) of robbery events in Chicago in 2016.3 ... Data can be found at https://data.cityofchicago.org. |
| Dataset Splits | Yes | We divide the sample {xi}n i=1 into two disjoint training and test sets, and use the training set to compute \ FSSD2 \hat\sigma_{H1}+\gamma , where a small regularization parameter \gamma > 0 is added for numerical stability. All tests with optimization use 20% of the sample size n for parameter tuning. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, TensorFlow X.Y) that were used in the experiments. |
| Experiment Setup | Yes | We evaluate the following six kernel-based nonparametric tests with α = 0.05, all using the Gaussian kernel. All tests with optimization use 20% of the sample size n for parameter tuning. For FSSD tests, we use J = 5. |