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..
Continuous-time Models for Stochastic Optimization Algorithms
Authors: Antonio Orvieto, Aurelien Lucchi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify this result on a one dimensional quadratic, under the choice of parameters in our example, using Euler-Maruyama simulation (i.e. PGD) with h = 10 3, σ = 5. In Fig. 1 we show the mean and standard deviation relative to 20 realization of the Gaussian noise. |
| Researcher Affiliation | Academia | Antonio Orvieto Department of Computer Science ETH Zurich, Switzerland Aurelien Lucchi Department of Computer Science ETH Zurich, Switzerland |
| Pseudocode | No | The paper presents mathematical equations and descriptions of algorithms (e.g., MB-PGD, VR-PGD) but does not include any formally labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The empirical verification is performed on 'a one dimensional quadratic', which is a synthetic function used for simulation and not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes a simulation on a synthetic one-dimensional quadratic function and does not provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions 'using Euler-Maruyama simulation' but does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running this simulation. |
| Software Dependencies | No | The paper mentions 'Euler-Maruyama simulation' as the method used, but does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, or simulation software with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We verify this result on a one dimensional quadratic, under the choice of parameters in our example, using Euler-Maruyama simulation (i.e. PGD) with h = 10 3, σ = 5. |