Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

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 inference in high-dimensional data, and I am currently working on using Hamiltonian methods in Variational Inference.



  • A. Caterini , R. Cornish , D. Sejdinovic , A. Doucet , Variational Inference with Continuously-Indexed Normalizing Flows, in Uncertainty in Artificial Intelligence (UAI), 2021.


  • R. Cornish , A. Caterini , G. Deligiannidis , A. Doucet , Relaxing bijectivity constraints with continuously indexed normalising flows, in ICML, 2020, 2133–2143.


  • D. Watson-Parris , S. Sutherland , M. Christensen , A. Caterini , D. Sejdinovic , P. Stier , Detecting Anthropogenic Cloud Perturbations with Deep Learning, in ICML 2019 Workshop on Climate Change: How Can AI Help?, 2019.


  • A. Caterini , A. Doucet , D. Sejdinovic , Hamiltonian Variational Auto-Encoder, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  • A. Caterini , D. E. Chang , Deep Neural Networks in a Mathematical Framework. Springer, 2018.


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


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


  • 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.