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.
Nonparametric Score Estimators
Authors: Yuhao Zhou, Jiaxin Shi, Jun Zhu
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 20,006 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,510 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 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 <EMAIL>. |
| 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. |