336 citations · 1k across the 37 of their papers we have counts for
37 papers
Multi-group Gaussian Processes
Didong Li, Andrew Jones, Sudipto Banerjee +1
Gaussian processes (GPs) are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Modern scientific data sets are typi…
Nonnegative spatial factorization
F. William Townes, Barbara E. Engelhardt
Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable appr…
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Guillaume Martinet, Alexander Strzalkowski, Barbara E. Engelhardt
Selecting powerful predictors for an outcome is a cornerstone task for machine learning. However, some types of questions can only be answered by identifying the predictors that ca…
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Aishwarya Mandyam, Andrew Jones, Jiayu Yao +2
Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due…
Active multi-fidelity Bayesian online changepoint detection
Gregory W. Gundersen, Diana Cai, Chuteng Zhou +2
Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as…
Contrastive latent variable modeling with application to case-control sequencing experiments
Andrew Jones, F. William Townes, Didong Li +1
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state t…