Stochastic Approximation Algorithms for Systems of Interacting Particles
Authors: Mohammad Reza Karimi Jaghargh, Ya-Ping Hsieh, Andreas Krause
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a novel framework that establishes a precise connection between these discrete-time schemes and their corresponding mean-field limits in terms of convergence properties and asymptotic behavior. Our paper aims to bridge this gap by rigorously establishing the convergence of discrete-time algorithms to their continuous-time counterparts in terms of long-term behavior. |
| Researcher Affiliation | Academia | Mohammad Reza Karimi ETH Zürich mkarimi@inf.ethz.ch Ya-Ping Hsieh ETH Zürich yaping.hsieh@inf.ethz.ch Andreas Krause ETH Zürich krausea@ethz.ch |
| Pseudocode | No | The paper presents mathematical formulations and algorithmic templates like (SAA) and (PSAA) but does not include any clearly labeled "Pseudocode" or "Algorithm" blocks with structured steps. |
| Open Source Code | No | The paper does not contain any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving dataset usage, training, or evaluation, thus no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not provide any specific details about hardware used for experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with version numbers required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameter values or system-level training configurations. |