9 citations · 10 across the 5 of their papers we have counts for
6 papers
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Jimmy Hickey, Jonathan P. Williams, Emily C. Hector
Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another populati…
Fused mean structure learning in data integration with dependence
Emily C. Hector
Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vect…
Distributed Inference for Spatial Extremes Modeling in High Dimensions
Emily C. Hector, Brian J. Reich
Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs). MSPs are computationally prohibitiv…
Parallel-and-stream accelerator for computationally fast supervised learning
Emily C. Hector, Lan Luo, Peter X. -K. Song
Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce…
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Emily C. Hector, Peter X. -K. Song
This paper presents a unified framework for supervised learning and inference procedures using the divide-and-conquer approach for high-dimensional correlated outcomes. We propose…
A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis
Emily C. Hector, Peter X. -K. Song
This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations.…