Seth Flaxman

Seth Flaxman

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

Seth was 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.

Seth is now a lecturer in the statistics section of the Department of Mathematics at Imperial College London, joint with the Data Science Institute.

Publications

2019

  • S. Flaxman , M. Chirico , P. Pereira , C. Loeffler , Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “Real-Time Crime Forecasting Challenge,” Revised and resubmit at Annals of Applied Statistics, 2019.
    Project: bigbayes

2018

  • C. Loeffler , S. Flaxman , Is gun violence contagious? A spatiotemporal test, Journal of Quantitative Criminology, vol. 34, no. 4, 999–1017, 2018.
    Project: bigbayes
  • H. Law , D. Sejdinovic , E. Cameron , T. Lucas , S. Flaxman , K. Battle , K. Fukumizu , Variational Learning on Aggregate Outputs with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
    Project: bigbayes
  • J. Ton , S. Flaxman , D. Sejdinovic , S. Bhatt , Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features, Spatial Statistics, vol. 28, 59–78, 2018.
    Project: bigbayes
  • H. Law , D. Sutherland , D. Sejdinovic , S. Flaxman , Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.
    Project: bigbayes

2017

  • B. Goodman , S. Flaxman , European Union Regulations on Algorithmic Decision Making and a “Right to Explanation,” AI Magazine, vol. 38, no. 3, 50–58, 2017.
    Project: bigbayes
  • S. Flaxman , Y. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 2017.
    Project: bigbayes
  • 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

  • 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.
  • 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
  • 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
  • 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

Software

2017

  • S. Flaxman , M. Chirico , P. Pereira , C. Loeffler , Forecasting Crime in Portland. 2017.
    Project: bigbayes
  • S. Flaxman , Y. W. Teh , D. Sejdinovic , Kernel Poisson. 2017.
    Project: bigbayes