4 papers
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…
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…
Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimation
Andrea Thomas Nava, Lijo Johny, Fabio Azzalini +2
Data-Driven Product Development (DDPD) leverages data to learn the relationship between product design specifications and resulting properties. To discover improved designs, we tra…