Seth is a postdoc working on scalable methods for spatiotemporal statistics and Bayesian machine learning, applied to public policy / social science areas including crime and public health. He completed his PhD at Carnegie Mellon University in August 2015 in a program that is joint between public policy and machine learning.
Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017, to appear.
Wilbert Van Panhuis,
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016, 1013–1021.
Is Gun Violence Contagious?, 2016.
European Union regulations on algorithmic decision-making and a “right to explanation,” Jun-2016.
Yee Whye Teh,
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata, Arxiv e-prints, Nov-2016.
D. J. Weiss,
D. L. Smith,
P. W. Gething,
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation, Dec-2016.
Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
Y. W. Teh,
Tucker Gaussian Process for Regression and Collaborative Filtering, 2016.