Nonparametric Score Estimators
Authors: Yuhao Zhou, Jiaxin Shi, Jun Zhu
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our estimators on both synthetic and real data. In Sec. 5.1, we consider a challenging grid distribution as described in the experiment of Sutherland et al. (2018) to test the accuracy of nonparametric score estimators in high dimensions and out-of-sample points, In Sec. 5.2 we train Wasserstein autoencoders (WAE) with score estimation and compare the accuracy and the efficiency of different estimators. |
| Researcher Affiliation | Academia | Dept. of Comp. Sci. & Tech., BNRist Center, Institute for AI, Tsinghua-Bosch ML Center, Tsinghua University. Correspondence to: J. Zhu <dcszj@tsinghua.edu.cn>. |
| Pseudocode | Yes | We describe the full algorithm in Example C.4 (appendix C.4.3). |
| Open Source Code | Yes | Code is available at https://github.com/miskcoo/kscore. |
| Open Datasets | Yes | We train WAEs on MNIST and Celeb A and repeat each configuration 3 times. The average negative log-likelihoods for MNIST estimated by AIS (Neal, 2001) are reported in Table 2. The results for Celeb A are reported in appendix A. |
| Dataset Splits | No | The paper mentions using MNIST and Celeb A datasets but does not explicitly detail the training, validation, and test splits used for reproducibility. |
| Hardware Specification | Yes | All models are timed on Ge Force GTX TITAN X GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We train WAEs on MNIST and Celeb A and repeat each configuration 3 times. The average negative log-likelihoods for MNIST estimated by AIS (Neal, 2001) are reported in Table 2. KEF-CG for λ = 10 5 on MNIST. We report the result of 32 runs in Fig. 1. |