Random Feature Stein Discrepancies
Authors: Jonathan Huggins, Lester Mackey
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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. |