From the 1 of 12 linked papers with an AI index.
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stat.ML2026
Spectrally Deconfounded Gradient Boosting
Andrea Nava, Peter Bühlmann, Fabio Sigrist
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfo…
stat.ML2026
Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalability to large data sets is limited by co…