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

2018

  • H. Law , D. Sejdinovic , E. Cameron , T. Lucas , S. Flaxman , K. Battle , K. Fukumizu , Variational Learning on Aggregate Outputs with Gaussian Processes, ArXiv e-prints:1805.08463, 2018.
  • J. Ton , S. Flaxman , D. Sejdinovic , S. Bhatt , Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features, Spatial Statistics, to appear, 2018.
  • H. Law , D. Sutherland , D. Sejdinovic , S. Flaxman , Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.

2017

  • S. Flaxman , Y. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 2017.
  • Q. Zhang , S. Filippi , S. Flaxman , D. Sejdinovic , Feature-to-Feature Regression for a Two-Step Conditional Independence Test, in Uncertainty in Artificial Intelligence (UAI), 2017.
    Project: bigbayes
  • J. Runge , P. Nowack , M. Kretschmer , S. Flaxman , D. Sejdinovic , Detecting Causal Associations in Large Nonlinear Time Series Datasets, ArXiv e-prints:1702.07007, 2017.
  • S. Flaxman , Y. W. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017.
    Project: bigbayes

2016

  • W. Herlands , A. Wilson , H. Nickisch , S. Flaxman , D. Neill , W. Van Panhuis , E. 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
  • C. Loeffler , S. Flaxman , Is Gun Violence Contagious?, 2016.
    Project: bigbayes
  • B. Goodman , S. Flaxman , European Union regulations on algorithmic decision-making and a “right to explanation,” Jun-2016.
    Project: bigbayes
  • S. Flaxman , D. Sutherland , Y. Wang , Y. W. 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 , S. Flaxman , D. J. Weiss , D. L. Smith , P. W. Gething , Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation, Dec-2016.
  • H. Kim , X. Lu , S. Flaxman , Y. W. Teh , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 2016.
  • S. Flaxman , D. Sejdinovic , J. 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 , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 2016.

Software

2017