A Latent Variational Framework for Stochastic Optimization
Authors: Philippe Casgrain
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a latent stochastic variational problem. Using techniques from stochastic control, the solution to the variational problem is shown to be equivalent to that of a Forward Backward Stochastic Differential Equation (FBSDE). |
| Researcher Affiliation | Academia | Philippe Casgrain Department of Statistical Sciences University of Toronto Toronto, ON, Canada p.casgrain@mail.utoronto.ca |
| Pseudocode | No | The paper contains mathematical derivations and equations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical framework and does not conduct experiments involving training on a dataset. It refers to "training points" in the context of defining the problem and gradient estimation, but not as part of an empirical training process. |
| Dataset Splits | No | The paper is theoretical and does not mention any validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |