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..
Gradient-Free Kernel Stein Discrepancy
Authors: Matthew Fisher, Chris J. Oates
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The practical performance of GF-KSD remains to be assessed. Suitable choices for both q and the kernel parameters σ and β are proposed and investigated in Section 3, and practical demonstrations of GF-KSD are presented in Section 4. |
| Researcher Affiliation | Academia | 1Newcastle University, UK 2Alan Turing Institute, UK |
| Pseudocode | No | The paper describes methods using prose and mathematical notation but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Python code to reproduce the experiments reported below can be downloaded at [blinded]. |
| Open Datasets | Yes | The data analysed are due to Hewitt [1921], and full details are contained in Appendix C.3. |
| Dataset Splits | No | The paper discusses 'sequences (πn)n N' and 'samples (xn)n N' in its experiments, and describes optimization processes, but does not provide explicit training/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | Yes | In order to obtain independent samples from the posterior for comparison, we utilised Stan [Stan Development Team, 2022]... URL https://mc-stan.org/. R package version 2.21.5. |
| Experiment Setup | Yes | The Laplace approximation was obtained by the use of 48 iterations of the L-BFGS optimisation algorithm... The stochastic optimisation routine used was Adam [Kingma and Ba, 2015] with learning rate 0.001. Due to issues involving exploding gradients due to the q/p term in GF-KSD, we utilised gradient clipping... with the maximum 2-norm value taken to be 30. In the banana experiment, the dimensionality of the hidden units in the underlying autoregressive neural network was taken as 20. |