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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stochastic Approximation Algorithms for Systems of Interacting Particles
Authors: Mohammad Reza Karimi Jaghargh, Ya-Ping Hsieh, Andreas Krause
NeurIPS 2023 | Venue PDF | 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 EMAIL Ya-Ping Hsieh ETH Zürich EMAIL Andreas Krause ETH Zürich EMAIL |
| 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. |