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07/24/2023
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
The Path-Dependent Neural Jump ODE (PD-NJ-ODE) is a model for predicting...
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06/28/2022
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
This paper studies the problem of forecasting general stochastic process...
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04/28/2021
Optimal Stopping via Randomized Neural Networks
This paper presents new machine learning approaches to approximate the s...
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06/08/2020
Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN
Continuous stochastic processes are widely used to model time series tha...
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04/28/2020
Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices
We introduce Denise, a deep learning based algorithm for decomposing pos...
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04/27/2020
