Add to ORKGThis paper studies the problem of forecasting general stochastic processes using an extension of the Neural Jump ODE (NJ-ODE) framework. While NJ-ODE was the first framework to establish convergence guarantees for the prediction of irregularly observed time series, these results were limited to data stemming from It\^o-diffusions with complete observations, in particular Markov processes where all coordinates are observed simultaneously. In this work, we generalise these results to generic, possibly non-Markovian or discontinuous, stochastic processes with incomplete observations, by utilising the reconstruction properties of the signature transform. These theoretical results are supported by empirical studies, where it is shown that the pa...
A Neural Process (NP) is a map from a set of observed input-output pairs to a predictive distributio...
AbstractConsider a p-variate counting process N = (N(i)) with jump times {τ(i)1, τ(i)2, …}. Suppose ...
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics...
Most machine learning methods are used as a black box for modelling. We may try to extract some know...
In this article, we employ a collection of stochastic differential equations with drift and diffusio...
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, c...
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by s...
Forecasting the likelihood, timing, and nature of events is a major goal of modeling stochastic dyna...
We introduce a machine-learning framework named statistics-informed neural network (SINN) for learni...
we consider a variant of the conventional neural network model, called the stochastic neural network...
This thesis establishes control and estimation architectures that combine both model-based and model...
We show that Neural ODEs, an emerging class of timecontinuous neural networks, can be verified by so...
Continuous-time Markov chains are used to model stochastic systems where transitions can occur at ir...
Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling ...
AbstractWe consider the multivariate point process determined by the crossing times of the component...
A Neural Process (NP) is a map from a set of observed input-output pairs to a predictive distributio...
AbstractConsider a p-variate counting process N = (N(i)) with jump times {τ(i)1, τ(i)2, …}. Suppose ...
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics...
Most machine learning methods are used as a black box for modelling. We may try to extract some know...
In this article, we employ a collection of stochastic differential equations with drift and diffusio...
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, c...
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by s...
Forecasting the likelihood, timing, and nature of events is a major goal of modeling stochastic dyna...
We introduce a machine-learning framework named statistics-informed neural network (SINN) for learni...
we consider a variant of the conventional neural network model, called the stochastic neural network...
This thesis establishes control and estimation architectures that combine both model-based and model...
We show that Neural ODEs, an emerging class of timecontinuous neural networks, can be verified by so...
Continuous-time Markov chains are used to model stochastic systems where transitions can occur at ir...
Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling ...
AbstractWe consider the multivariate point process determined by the crossing times of the component...
A Neural Process (NP) is a map from a set of observed input-output pairs to a predictive distributio...
AbstractConsider a p-variate counting process N = (N(i)) with jump times {τ(i)1, τ(i)2, …}. Suppose ...
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics...