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..
A Dynamical System View of Langevin-Based Non-Convex Sampling
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 | To address these issues, we develop a novel framework that lifts the above issues by harnessing several tools from the theory of dynamical systems. Our key result is that, for a large class of state-of-the-art sampling schemes, their last-iterate convergence in Wasserstein distances can be reduced to the study of their continuous-time counterparts, which is much better understood. Coupled with standard assumptions of MCMC sampling, our theory immediately yields the last-iterate Wasserstein convergence of many advanced sampling schemes such as mirror Langevin, proximal, randomized mid-point, and Runge-Kutta methods. |
| Researcher Affiliation | Academia | Mohammad Reza Karimi ETH Zürich EMAIL Ya-Ping Hsieh ETH Zürich EMAIL Andreas Krause ETH Zürich EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and descriptions (e.g., (LRM), (RMM), (ORMM)), but it does not provide any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | This is a theoretical paper and does not involve training on datasets. Therefore, it does not mention public datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments with data splits. Therefore, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not report on empirical experiments. Therefore, it does not describe the hardware used to run experiments. |
| Software Dependencies | No | This is a theoretical paper and does not report on empirical experiments. Therefore, it does not provide specific version numbers for software dependencies. |
| Experiment Setup | No | This is a theoretical paper and does not report on empirical experiments. Therefore, it does not provide details about an experimental setup, such as hyperparameters or training settings. |