arXiv:2607. 21083v1 Announce Type: cross Abstract: B-spline regression constitutes a widely used framework for nonparametric modeling.
arXiv:2604. 05635v2 Announce Type: replace Abstract: Numerical preprocessing remains a critical component of tabular deep learning, as the representation of continuous features can strongly affect downstream performance.
arXiv:2607. 21098v1 Announce Type: new Abstract: Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time.
arXiv:2507. 06637v2 Announce Type: replace-cross Abstract: We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates.
arXiv:2602. 22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis.
arXiv:2508. 14268v2 Announce Type: replace-cross Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest.
arXiv:2602. 20857v2 Announce Type: replace-cross Abstract: Segmented curve fitting remains an essential approach for the comprehensive analysis of local patterns in non-stationary time-series data.
arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.
arXiv:2505. 00571v3 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors.
arXiv:2607. 01752v1 Announce Type: new Abstract: Temporal point processes (TPPs) have widespread applications across various domains.
arXiv:2605. 23272v2 Announce Type: replace-cross Abstract: Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data.
arXiv:2608. 11567v1 Announce Type: new Abstract: In real-world scenarios, the training data usually contains redundant features, label noise and feature noise, which provide severe challenges for the efficiency of machine learning methods.
arXiv:2607. 06026v1 Announce Type: new Abstract: We present a novel isogeometric deep learning method, termed SplineNet, for the seamless design and analysis of shell structures with complex geometries.