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Found 5 papers, 5 papers with code
Learning the conditional law: signatures and conditional GANs in filtering and prediction of diffusion processes
We consider the filtering and prediction problem for a diffusion process.
Sig-Wasserstein GANs for Time Series Generation
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Robust pricing and hedging via neural SDEs
Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data.
Multi-index Antithetic Stochastic Gradient Algorithm
In this paper, we construct a Multi-index Antithetic Stochastic Gradient Algorithm (MASGA) whose implementation is independent of the structure of the target measure and which achieves performance on par with Monte Carlo estimators that have access to unbiased samples from the distribution of interest.
Conditional Sig-Wasserstein GANs for Time Series Generation
The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
