Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Langevin Quasi-Monte Carlo
Authors: Sifan Liu
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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. |