Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Authors: Aniket Das, Dheeraj Nagaraj
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments (Section 8) show that GB-SVGD obtains similar performance as SVGD but requires fewer computations. 8 Experiments We compare the performance of GB-SVGD and SVGD. We take n = 100 and use the Laplace kernel with h = 1 for both. We pick the stepsize γ by a grid search for each algorithm. Additional details are presented in Appendix G. We observe that SVGD takes fewer iterations to converge, but the compute time for GB-SVGD is lower. |
| Researcher Affiliation | Industry | Aniket Das Google Research Bangalore, India ketd@google.com Dheeraj Nagaraj Google Research Bangalore, India dheerajnagaraj@google.com |
| Pseudocode | Yes | Algorithm 1 Virtual Particle SVGD (VP-SVGD) Algorithm 2 Global Batch SVGD (GB-SVGD) |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Sampling from Isotropic Gaussian (Figure 1): As a sanity check, we set π = N(0, I) with d = 5. Bayesian Logistic Regression (Figure 2) We consider the Covertype dataset which contains 580, 000 data points with d = 54. We consider the same priors suggested in Gershman et al. [16] and implemented in Liu and Wang [31]. |
| Dataset Splits | No | The paper mentions using datasets but does not explicitly provide specific train, test, or validation split percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The paper does not explicitly describe specific hardware components such as GPU/CPU models, memory, or detailed computing environments used for running the experiments. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., Python, specific libraries, or frameworks with their versions) that were used for the experiments. |
| Experiment Setup | Yes | We take n = 100 and use the Laplace kernel with h = 1 for both. We pick the stepsize γ by a grid search for each algorithm. We pick K = 10 for GB-SVGD. We take K = 40 for GB-SVGD. For both VP-SVGD and GB-SVGD, we use Ada Grad with momentum to set the step-sizes as per Liu and Wang [31] |