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 [1].
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. |