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..
Random Feature Stein Discrepancies
Authors: Jonathan Huggins, Lester Mackey
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with sampler selection for approximate posterior inference and goodness-of-fit testing, RΦSDs perform as well or better than quadratic-time KSDs while being orders of magnitude faster to compute. |
| Researcher Affiliation | Collaboration | Jonathan H. Huggins Department of Biostatistics, Harvard Lester Mackey Microsoft Research New England |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | See https://bitbucket.org/jhhuggins/random-feature-stein-discrepancies for our code. |
| Open Datasets | No | The paper uses standard datasets like the bimodal Gaussian mixture model and multivariate Gaussian/Laplace/t-distributions, which are widely known, but it does not provide explicit access information (link, DOI, specific citation with author/year) for these datasets in the text. It refers to a previous paper for the GMM target but does not explicitly link or cite it here for the dataset. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test splits for the datasets used in the experiments. It mentions using a small subsample of the full dataset for certain calculations, but not explicit splits for model training/evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | Yes | In particular, we chose = γ/3, λ = 1 /2, and = 4 /(2 + ). Except for the importance sample efficiency experiments, where we varied γ explicitly, all experiments used γ = 1/4. Let d medu denote the estimated median of the distance between data points under the u-norm, where the estimate is based on using a small subsample of the full dataset. For L2 Sech Exp, we took a 1 = 2 d med1, except in the sample quality experiments where we set a 1 = 2 . For most experiments we took β = 1/2, c = 4 d med2, and df = 0.5. The exceptions were in the sample quality experiments, where we set c = 1, and the restricted Boltzmann machine testing experiment, where we set c = 10 d med2 and df = 2.5. |