A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Authors: Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the validity of the rates of Corollary 6 with simple experiments provided Section 13. |
| Researcher Affiliation | Academia | Anna Korba Gatsby Computational Neuroscience Unit University College London a.korba@ucl.ac.uk Adil Salim Visual Computing Center KAUST adil.salim@kaust.edu.sa Michael Arbel Gatsby Computational Neuroscience Unit University College London michael.n.arbel@gmail.com Giulia Luise Computer Science Department University College London g.luise16@ucl.ac.uk Arthur Gretton Gatsby Computational Neuroscience Unit University College London arthur.gretton@gmail.com |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not contain any statement about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper mentions "toy experiments are deferred to the appendix" but does not specify any publicly available datasets used for training in the main text. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits. It mentions "toy experiments" but defers details to the appendix. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software with specific version numbers. |
| Experiment Setup | No | The paper mentions "toy experiments are deferred to the appendix" but does not provide specific details about the experimental setup, hyperparameters, or system-level training settings in the main text. |