Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Authors: Wenkai Xu, Gesine D Reinert
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic distributions and trained generative models on synthetic and real datasets illustrate that the method shows improved power performance compared to existing approaches. |
| Researcher Affiliation | Academia | Wenkai Xu Department of Statistics University of Oxford EMAIL, Gesine Reinert Department of Statistics University of Oxford and Alan Turing Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Estimating the conditional probability via summary statistics, Algorithm 2 Assessment procedures for implicit generative models |
| Open Source Code | Yes | The code is available at https://github.com/wenkaixl/npksd.git. |
| Open Datasets | Yes | MNIST Dataset This dataset contains 28 28 grey-scale images of handwritten digits [Le Cun et al., 1998]; CIFAR10 Dataset This dataset contains 32 32 RGB coloured images [Krizhevsky, 2009] |
| Dataset Splits | No | The paper mentions training and test samples but does not explicitly provide details about a validation set split or how to create one for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU models, or specific cloud instance types. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers, such as libraries, frameworks, or operating systems used for the experiments. |
| Experiment Setup | Yes | For our experiments, 600 samples are generated from the generative models and 100 samples are used for the test; the samples are then down-sampled into 7 7 images, as in Schrab et al. [2021]. The test level is α = 0.05.; For our experiments, 800 samples are generated from the generative models and 200 samples are used for the test; the samples are then down-sampled into 8 8 images, in a similar fashion as in Schrab et al. [2021]. The test level is α = 0.05. |