Langevin Quasi-Monte Carlo
Authors: Sifan Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical analysis is supported by compelling numerical experiments, which demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Sifan Liu Department of Statistics Stanford University Stanford, CA 94305 sfliu@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Langevin quasi-Monte Carlo (LQMC) |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | To investigate the performance of LQMC in a posterior prediction setting, we conducted experiments similar to those presented in Dubey et al. (2016) using three UCI datasets. Each dataset was split into a training set (70%), a validation set (10%), and a test set (20%). |
| Dataset Splits | Yes | Each dataset was split into a training set (70%), a validation set (10%), and a test set (20%). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or specific tools). |
| Experiment Setup | Yes | The step size h is fixed to 0.001. ... at each iteration, we estimate the gradient using a random subset of 10 observations. ... We will compare the performance of the LQMC algorithm using three different step sizes: a constant step size of 10 4, a constant step size of 10 2, and decreasing step sizes with hk = c0(c1 + k) 1/3. ... Each iteration computes the stochastic gradient using 32 data points sampled at random. |