Showing stat.MLShow all
3 papers · 1 filter
stat.ML2026
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…
stat.ML2025
STACI: Spatio-Temporal Aleatoric Conformal Inference
Brandon R. Feng, David Keetae Park, Xihaier Luo +3
Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while s…
stat.ML2024
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
Brandon R. Feng, Reetam Majumder, Brian J. Reich +1
Gaussian processes (GPs) are a ubiquitous tool for geostatistical modeling with high levels of flexibility and interpretability, and the ability to make predictions at unseen spati…