Stein Variational Gradient Descent as Moment Matching
Authors: Qiang Liu, Dilin Wang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results provide a theoretical framework for analyzing properties of SVGD with different kernels, shedding insight into optimal kernel choice. Our Results We analyze the finite sample properties of SVGD. |
| Researcher Affiliation | Academia | Qiang Liu, Dilin Wang Department of Computer Science The University of Texas at Austin Austin, TX 78712 {lqiang, dilin}@cs.utexas.edu |
| Pseudocode | No | The paper describes algorithms and concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using specific datasets, thus no information on dataset availability is provided. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe empirical experiments with dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not provide details on specific hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and does not provide details on specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe specific experimental setup details, hyperparameters, or training configurations. |