activity
20172025
most citedAccurate Uncertainty Estimation and Decomposition in Ensemble Learning

34 citations · 79 across the 12 of their papers we have counts for

collaborators

13 papers

stat.ME2025

Everything all at once: On choosing an estimand for multi-component environmental exposures

Kara E. Rudolph, Shodai Inose, Nicholas Williams +4

Many research questions -- particularly those in environmental health -- do not involve binary exposures. In environmental epidemiology, this includes multivariate exposure mixture…

stat.ME2024★ 9 cited

Penalized Distributed Lag Interaction Model: Air Pollution, Birth Weight and Neighborhood Vulnerability

Danielle Demateis, Kayleigh P. Keller, David Rojas-Rueda +2

Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollut…

stat.ME2023★ 2 cited

Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study

Melanie N. Mayer, Arce Domingo-Relloso, Marianthi-Anna Kioumourtzoglou +3

The estimation of the effect of environmental exposures and overall mixtures on a survival time outcome is common in environmental epidemiological studies. While advanced statistic…

stat.AP2021★ 1 cited

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

Elizabeth A. Gibson, Junhui Zhang, Jingkai Yan +7

Environmental health researchers often aim to identify sources/behaviors that give rise to potentially harmful exposures. We adapted principal component pursuit (PCP)-a robust tech…

stat.ME2021★ 12 cited

Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution

Daniel Mork, Marianthi-Anna Kioumourtzoglou, Marc Weisskopf +2

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibi…

stat.AP2020

Integrated causal-predictive machine learning models for tropical cyclone epidemiology

Rachel C. Nethery, Nina Katz-Christy, Marianthi-Anna Kioumourtzoglou +3

Strategic preparedness has been shown to reduce the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protectiv…

stat.AP2020

Reflection on modern methods: Good practices for applied statistical learning in epidemiology

Yanelli Nunez, Elizabeth A. Gibson, Eva M. Tanner +4

Statistical learning (SL) includes methods that extract knowledge from complex data. SL methods beyond generalized linear models are being increasingly implemented in public health…

cs.LG2019★ 34 cited

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1

Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complet…

stat.ME2019★ 1 cited

On the Interplay Between Exposure Misclassification and Informative Cluster Size

Glen McGee, Marianthi-Anna Kioumourtzoglou, Marc G. Weisskopf +2

In this paper we study the impact of exposure misclassification when cluster size is potentially informative (i.e., related to outcomes) and when misclassification is differential…

stat.ME2019★ 1 cited

Spatially Adaptive Ensemble Learning with Calibrated Predictive Uncertainty

Yanran Li, Jeremiah Zhe Liu, John Paisley +2

Air pollution exposure assessment often uses ensembles of spatio-temporal models, but a critical limitation is that conventional methods use deterministic weights and fail to quant…

cs.LG2018★ 1 cited

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process

Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1

Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…

stat.ME2018★ 16 cited

Matching on Generalized Propensity Scores with Continuous Exposures

Xiao Wu, Fabrizia Mealli, Marianthi-Anna Kioumourtzoglou +2

In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still…

stat.ME2017★ 2 cited

Causal inference in the context of an error prone exposure: air pollution and mortality

Xiao Wu, Danielle Braun, Marianthi-Anna Kioumourtzoglou +3

We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS). U…