Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
Authors: Than Huy Nguyen, Umut Simsekli, Gael Richard
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this study, we analyze the non-asymptotic behavior of FLMC for nonconvex optimization and prove finite-time bounds for its expected suboptimality. Our results show that the weak-error of FLMC increases faster than LMC, which suggests using smaller step-sizes in FLMC. |
| Researcher Affiliation | Academia | 1LTCI, T el ecom Paris Tech, Universit e Paris-Saclay, 75013, Paris, France. |
| Pseudocode | No | The paper describes algorithms using mathematical equations (e.g., Equation 2 for ULA, Equation 3 for FLA) and iterative schemes, but it does not provide any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical analysis of algorithms. It does not describe any experimental training on datasets, nor does it provide access information for any datasets used by the authors. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups with dataset splits. It does not provide any specific information about training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe conducting experiments that would require specific hardware. No hardware specifications (e.g., GPU, CPU models, or memory) are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe conducting experiments that would require specific software dependencies. No software names with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis. It defines parameters within the algorithms (e.g., step-size η, inverse temperature β) for theoretical discussion, but it does not describe an experimental setup with hyperparameter values, training schedules, or system-level settings for actual experiments. |