80 citations · 548 across the 37 of their papers we have counts for
40 papers
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee
Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x. However, these distrib…
Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance
Elizabeth Cucuzzella, Tria McNeely, Kimberly Wood +1
Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far fr…
Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance
Trey McNeely, Pavel Khokhlov, Niccolo Dalmasso +2
Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to sho…
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Luca Masserano, Tommaso Dorigo, Rafael Izbicki +2
Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especia…
Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)
Biprateep Dey, David Zhao, Brett H. Andrews +3
Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable given complex inp…
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
Trey McNeely, Galen Vincent, Kimberly M. Wood +2
Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this…