Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Authors: Xuechen Li, Yi Wu, Lester Mackey, Murat A. Erdogdu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical studies show that these algorithms lead to better stability and lower asymptotic errors. |
| Researcher Affiliation | Collaboration | University of Toronto1, Vector Institute2, Microsoft Research3 {lxuechen, dennywu, erdogdu}@cs.toronto.edu, lmackey@microsoft.com |
| Pseudocode | No | The paper presents mathematical update rules (equations 9 and 10) but not in a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper describes generating data for the Bayesian logistic regression and mentions 'Gaussian mixture' as a problem, but it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | To characterize the true posterior, we sample 50k particles driven by EM with a step size of 0.001 until convergence. We subsample from these particles 5k examples to represent the true posterior each time we intend to estimate squared W2. We monitor the kernel Stein discrepancy 5 (KSD) [29, 10, 36] using the inverse multiquadratic kernel [29] with hyperparameters β = 1/2 and c = 1 to measure the distance between the 100k particles and the true posterior. We confirm that these particles faithfully approximate the true posterior with the squared KSD being less than 0.002 in all settings. |