Seth Flaxman

Seth Flaxman

Scalable spatiotemporal statistics and Bayesian machine learning for public policy and social science

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.

Publications

2017

  • S. Flaxman, Y.W. Teh, D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017, to appear.
    Project: bigbayes

2016

  • William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing, Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016, 1013–1021.
    Project: sgmcmc
  • Charles Loeffler, Seth Flaxman, Is Gun Violence Contagious?, 2016.
    Project: bigbayes
  • Bryce Goodman, Seth Flaxman, European Union regulations on algorithmic decision-making and a “right to explanation,” Jun-2016.
    Project: bigbayes
  • Seth Flaxman, Dougal Sutherland, Yu-Xiang Wang, Yee Whye Teh, Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata, Arxiv e-prints, Nov-2016.
    Project: bigbayes
  • S. Bhatt, E. Cameron, Seth Flaxman, D. J. Weiss, D. L. Smith, P. W. Gething, Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation, Dec-2016.
  • S. Flaxman, D. Sejdinovic, J.P. Cunningham, S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes
  • H. Kim, X. Lu, S. Flaxman, Y. W. Teh, Tucker Gaussian Process for Regression and Collaborative Filtering, 2016.
    Project: bigbayes