Existing statistical learning guarantees for general kernel regressors often yield loose
bounds when used with finite-rank kernels. Yet, finite-rank kernels naturally appear in a …

Deep neural networks based on linear RNNs interleaved with position-wise MLPs are
gaining traction as competitive approaches for sequence modeling. Examples of such …

Time-series data in real-world settings typically exhibit long-range dependencies and are
observed at non-uniform intervals. In these settings, traditional sequence-based recurrent …

This study introduces an innovative methodology for mortality forecasting, which integrates
signature-based methods within the functional data framework of the Hyndman–Ullah (HU) …

We present a unified theory for Mahalanobis-type anomaly detection on Banach spaces,
using ideas from Cameron-Martin theory applied to non-Gaussian measures. This approach …

Randomised signature has been proposed as a flexible and easily implementable
alternative to the well-established path signature. In this article, we employ randomised …

We consider a stochastic volatility model where the dynamics of the volatility are described
by a linear function of the (time extended) signature of a primary process which is supposed …

We consider the problem of optimally sharing a financial position among agents with
potentially different reference risk measures. The problem is equivalent to computing the …

Controlled ordinary differential equations driven by continuous bounded variation curves
can be considered a continuous time analogue of recurrent neural networks for the …

One of the most challenging tasks in control theory is arguably the design of a regulator for
nonlinear systems when the dynamics are unknown. To tackle it, a popular strategy relies on …