4 citations · 4 across the 11 of their papers we have counts for
Showing 2026 · stat.MLShow all
2 papers · 2 filters
stat.ML2026
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…
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…