activity
20242026
collaborators

5 papers

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.ME2025

Causal Spatial Quantile Regression

Yan Gong, Reetam Majumder, Brian J. Reich +1

Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across…

stat.ME2025

Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud

Reetam Majumder, Benjamin A. Shaby, Brian J. Reich +1

We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit set. Characteristics of the…

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…