A Proximal Algorithm for Sampling
Authors: Jiaming Liang, Yongxin Chen
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a numerical example to illustrate our result. We consider sampling from a Gaussian-Laplace mixture... We run 500,000 iterations (with 100,000 burn-in iterations) for both the proximal sampling algorithm and LMC... Histograms and trace plots (of the 3-rd coordinate) of the samples generated by both methods are presented in Figures 1 and 2. |
| Researcher Affiliation | Academia | Jiaming Liang EMAIL Department of Computer Science, Yale University, New Haven, CT 06511. Yongxin Chen EMAIL School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332. |
| Pseudocode | Yes | Algorithm 1 Alternating Sampling Framework (Lee et al., 2021) Algorithm 2 RGO Rejection Sampling Algorithm 3 Accelerated Gradient Method |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code or a link to a code repository. The link provided in the paper (https: // openreview. net/ forum? id= Ck XOwlhf27) is for the open review process, not for code. |
| Open Datasets | No | We consider sampling from a Gaussian-Laplace mixture ν(x) = 0.5(2π) d/2p det Q exp( (x 1) Q(x 1)/2) + 0.5(2d) exp( 4x 1) where Q = USU , d = 5, S = diag(14, 15, 16, 17, 18), and U is an arbitrary orthogonal matrix. The dataset used in the computational results is a synthetic mixture defined within the paper and not an external publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes sampling from a Gaussian-Laplace mixture in the computational results section. It does not involve traditional datasets with training, validation, or test splits, as it is a sampling problem from a defined distribution, not a supervised or unsupervised learning task requiring data partitioning for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run the experiments or simulations. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | We run 500,000 iterations (with 100,000 burn-in iterations) for both the proximal sampling algorithm and LMC with η = 1/(Md) where d = 5 and M is as in (5) with (α, Lα) = (1, 27) and δ = 1. Histograms and trace plots (of the 3-rd coordinate) of the samples generated by both methods are presented in Figures 1 and 2. In addition, we also run 2,500,000 iterations (with 500,000 burn-in iterations) for LMC with η = 1/(5Md). |