arXiv:2401.10791v3 Announce Type: replace-cross Abstract: Training neural networks with first order optimisation methods is at the core of the empirical success of deep learning. The scale of initialisation is a crucial factor, as small initialisations are generally associated to a…
arXiv:2509.12166v1 Announce Type: new Abstract: Multivariate longitudinal data of mixed-type are increasingly collected in many science domains. However, algorithms to cluster this kind of data remain scarce, due to the challenge to simultaneously model the within- and between-time dependence structures…
arXiv:2504.08224v2 Announce Type: replace-cross Abstract: We propose an innovative design for an all-optical Echo State Network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enables fully optical implementation of arbitrary ESNs, featuring flexibility…
arXiv:2509.12185v1 Announce Type: new Abstract: Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in…
arXiv:2509.09070v2 Announce Type: replace-cross Abstract: Most explainable AI (XAI) frameworks are limited in their expressiveness, summarizing complex feature effects as single scalar values phi_i. This approach answers “what” features are important but fails to reveal “how” they interact. Furthermore, methods…
arXiv:2509.10613v1 Announce Type: cross Abstract: Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However,…
arXiv:2509.11907v1 Announce Type: cross Abstract: In this work, we consider the problem of identifying an unknown linear dynamical system given a finite hypothesis class. In particular, we analyze the effect of the excitation input on the sample complexity of identifying…
arXiv:2509.10626v1 Announce Type: cross Abstract: The Multimarginal Schr”odinger Bridge (MSB) finds the optimal coupling among a collection of random vectors with known statistics and a known correlation structure. In the MSB formulation, this correlation structure is specified emph{a priori} as…
arXiv:2509.12051v1 Announce Type: cross Abstract: Ambient air pollution poses significant health and environmental challenges. Exposure to high concentrations of PM$_{2.5}$ have been linked to increased respiratory and cardiovascular hospital admissions, more emergency department visits and deaths. Traditional air quality monitoring…
arXiv:2509.10632v1 Announce Type: cross Abstract: Nonlinear system identification often involves a fundamental trade-off between interpretability and flexibility, often requiring the incorporation of physical constraints. We propose a unified data-driven framework that combines the mathematical structure of the governing differential equations…
