Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction
Authors: Taiji Suzuki, Denny Wu, Atsushi Nitanda
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a general framework to prove a uniform-in-time propagation of chaos for MFLD that takes into account the errors due to finiteparticle approximation, time-discretization, and stochastic gradient. To demonstrate the wide applicability of our framework, we establish quantitative convergence rate guarantees to the regularized global optimal solution for (i) a wide range of learning problems such as mean-field neural network and MMD minimization, and (ii) different gradient estimators including SGD and SVRG. |
| Researcher Affiliation | Academia | Taiji Suzuki1,2, Denny Wu3,4, Atsushi Nitanda2,5 1University of Tokyo, 2RIKEN AIP, 3New York University, 4Flatiron Institute, 5Kyushu Institute of Technology |
| Pseudocode | No | The paper describes algorithms (F-MFLD, SGD-MFLD, SVRG-MFLD) in text but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on convergence analysis; it does not describe experiments performed on specific public datasets with access information. It mentions examples like "training data (zi, yi)" but this is a general reference within the problem setting, not an actual dataset used for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and focuses on convergence analysis; it does not present empirical experiments that would require training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on convergence analysis; it does not describe specific experimental setups with hyperparameters or training settings. |