4 citations · 4 across the 11 of their papers we have counts for
7 papers · 1 filter
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Andrea Nava, Ken Rölli, Armin Begic +1
Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most extensions of NPs focus on im…
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…
A Censored Transformed Model for Proportional Outcomes with Boundary Mass and an Application to Loss Given Default Modeling
Yuan Christopher Qiang, Fabio Sigrist
We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a cens…
A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
Pietro Colombo, Fabio Sigrist, Claire Miller +3
We propose a new scalable framework for spatio-temporal data fusion with multi-fidelity Gaussian processes (MFGPs) that enables fully likelihood-based inference for both stationary…
Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Andrea Nava, Fabio Sigrist
Laplace approximations are a standard tool for computationally efficient inference in latent Gaussian models, but they fail for quantile regression with the asymmetric Laplace like…
Selecting Hyperparameters for Tree-Boosting
Floris Jan Koster, Fabio Sigrist
Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article…
Scalable non-separable spatio-temporal Gaussian process models for large-scale short-term weather prediction
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Monitoring daily weather fields is critical for climate science, agriculture, and environmental planning, yet fully probabilistic spatio-temporal models become computationally proh…