Anthony Caterini

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High-Dimensional Statistics, Monte Carlo Methods, Applied Mathematics

I am a DPhil student in Statistics at the University of Oxford, supervised by Profs. Arnaud Doucet and Dino Sejdinovic. Generally, I am interested in statistical methods for high-dimensional data, and I am currently working on scaling Kernel Stein Discrepancies.

Publications

2017

• A. Caterini, A Novel Mathematical Framework for the Analysis of Neural Networks, Master's thesis, University of Waterloo, 2017.
  
  BibTeX

  @mastersthesis{caterini2017novel,
    title = {{A Novel Mathematical Framework for the Analysis of Neural Networks}},
    author = {Caterini, A.},
    year = {2017},
    school = {University of Waterloo}
  }
  

2016

• A. Caterini, D. E. Chang, A Geometric Framework for Convolutional Neural Networks, ArXiv e-prints:1608.04374, 2016.
  
  BibTeX

  arXiv

  @unpublished{caterini2016geometric,
    title = {A Geometric Framework for Convolutional Neural Networks},
    author = {Caterini, A. and Chang, D. E.},
    journal = {ArXiv e-prints:1608.04374},
    year = {2016}
  }
  

2015

• M. Winlaw, M. Hynes, A. Caterini, H. De Sterck, Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark, in Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on, 2015, 682–691.
  
  BibTeX

  @inproceedings{winlaw2015algorithmic,
    title = {{Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark}},
    author = {Winlaw, M. and Hynes, M. and Caterini, A. and De Sterck, H.},
    booktitle = {Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on},
    pages = {682--691},
    year = {2015},
    organization = {IEEE}
  }