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..
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo
Authors: ZHIYAN DING, Qin Li
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate numerical evidence in Section 6. Proofs are rather technical and are all left to appendices. |
| Researcher Affiliation | Academia | Zhiyan Ding Department of Mathematics University of Wisconsin-Madison Madison, WI 53706 EMAIL Qin Li Department of Mathematics University of Wisconsin-Madison Madison, WI 53706 EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomized Coordinate Averaging Decent O/U-LMC (RCAD-O/U-LMC) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper describes synthetic target distributions (e.g., N(0, Id) and f(x) = (x1 β 1)^2 + Ξ£(x_i)^2) and initial distributions, but does not refer to a publicly available dataset with concrete access information (link, DOI, citation). |
| Dataset Splits | No | The paper mentions running simulations with N = 5 * 10^5 particles and discusses initial distributions, but does not specify dataset splits (e.g., training, validation, test percentages or counts) as it deals with sampling from a distribution rather than using a fixed dataset. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In the ο¬rst example, our target distribution is N(0, Id) with d = 1000, and in the second example we use f(x) = (x1 β 1)^2 + Ξ£di=2 x2i. The initial distributions, for the overdamped and underdamped situations respectively, are N(0.5, Id) and N(0.5, I2d) in both exampes. We run both RCD-O/U-LMC and RCAD-O/U-LMC using N = 5 * 10^5 particles and test MSE error with Ο(x) = |x1|^2 in both examples. In all the computation, M is big enough. The improvement of adding variance reduction technique is obvious in both examples. |