Motivated by the paradigm of reservoir computing, we consider randomly initialized
controlled ResNets defined as Euler-discretizations of neural controlled differential …

Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …

Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …

Modeling stochastic and irregularly sampled time series is a challenging problem found in a
wide range of applications, especially in medicine. Neural stochastic differential equations …

Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for
modelling spatiotemporal PDE-dynamics under the influence of randomness. Based on the …

Multi-agent dynamical systems refer to scenarios where multiple units (aka agents) interact
with each other and evolve collectively over time. For instance, people's health conditions …

Recent years have witnessed the rise of misinformation campaigns that spread specific
narratives on social media to manipulate public opinions on different areas, such as politics …

Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …

This article provides a concise overview of some of the recent advances in the application of
rough path theory to machine learning. Controlled differential equations (CDEs) are …

The discovery of structure from time series data is a key problem in fields of study working
with complex systems. Most identifiability results and learning algorithms assume the …