arXiv:2509.10825v1 Announce Type: cross Abstract: We propose FACTORS, a framework that combines design of experiments with Shapley decomposition to address performance and stability issues that are sensitive to combinations of training factors. Our approach consistently estimates main effects and two-factor…
arXiv:2410.14090v2 Announce Type: replace Abstract: Projection-based model reduction is among the most widely adopted methods for constructing parametric Reduced-Order Models (ROM). Utilizing the snapshot data from solving full-order governing equations, the Proper Orthogonal Decomposition (POD) computes the optimal basis modes…
arXiv:2509.10693v1 Announce Type: cross Abstract: This work proposes a bid shading strategy for first-price auctions as a measure-valued optimization problem. We consider a standard parametric form for bid shading and formulate the problem as convex optimization over the joint distribution…
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…
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:2509.10626v1 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:2509.11907v1 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:2509.10613v1 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:2509.09070v2 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:2509.12185v1 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…
