Hey I'm Kai

Email | GitHub | Google Scholar | Curriculum Vitae

I am a third year Ph.D Student at Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, working with Charles Sutton. Before this, I completed my master supervised by Zoubin Ghahramani at Cambridge University Engineering Department. Research topics that I am interested in include approximate inference, (deep) generative models, and probabilistic programming.

I am one of the core developers of the Turing probabilistic programming language. I write efficient and robust software on Hamiltonian Monte Carlo methods in Julia (AdvancedHMC.jl).

Papers

  • Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino and Matthias Hennig. "Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference.", Neural Information Processing Systems, 2019. (arXiv)
  • Kai Xu, Akash Srivastava and Charles Sutton. "Variational Russian Roulette for Deep Bayesian Nonparametrics.", International Conference on Machine Learning, 2019. (abs, pdf, suppl, code)
  • Kai Xu, Akash Srivastava and Charles Sutton. "Amortized Inference for Latent Feature Models Using Variational Russian Roulette." NeurIPS Workshop for All of Bayesian Nonparametrics, 2018. (workshop, pdf)
  • Akash Srivastava, Kai Xu, Michael U. Gutmann and Charles Sutton. "Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models." arXiv preprint arXiv: 1806.00101, 2018. (arXiv, pdf)
  • Kai Xu, Dae Hoon Park, Yi Chang and Charles Sutton. "Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge." arXiv preprint arXiv: 1803.04042, 2018. (arXiv, pdf, demo, code, website)
  • Hong Ge, Kai Xu, and Zoubin Ghahramani. "Turing: A Language for Flexible Probabilistic Inference." International Conference on Artificial Intelligence and Statistics, 2018. (abs, pdf, code, website)

Talks

  • Presenter for "The Turing Language for Probabilistic Programming." at Julia Lab, MIT, MA, USA in June 2019
  • Presenter for "Variational Russian Roulette for Deep Bayesian Nonparametrics." at MIT-IBM Research, MIT, CA, USA in June 2019.
  • Presenter for "Variational Russian Roulette for Deep Bayesian Nonparametrics." at The 36th International Conference on Machine Learning, Long Beach, CA, USA in June 2019.
  • Copresenter for "The Turing Language for Probabilistic Programming." at The Inaugural International Conference on Probabilistic Programming, MIT, MA, USA in October 2018
  • Presenter for "Turing: a Fresh Approach to Probabilistic Programming." at JuliaCon 2017, Berkeley, CA, USA in June 2017 (video)

Experience

  • University of Edinburgh / Tutor for Probabilistic Modelling and Reasoning
    Jan. 2019 - Apr. 2019, Edinburgh, United Kingdom
  • University of Edinburgh / Teaching Assistant for Machine Learning and Pattern Recognition
    Sep. 2018 - Dec. 2018, Edinburgh, United Kingdom
  • Apple / Machine Learning Intern
    May 2018 - Aug. 2018, Cambridge, United Kingdom
  • University of Edinburgh / Teaching Assistant for Design Informatics
    Oct. 2017 - Dec. 2017, Edinburgh, United Kingdom
  • University fo Cambridge / Research Assistant at Machine Learning Group
    Nov. 2016 - Jul. 2017, Cambridge, United Kingdom

Education

  • University of Edinburgh / Ph.D in Informatics: Machine Learning
    Sep. 2017 - PRESENT, Edinburgh, United Kingdom
  • University of Cambridge / M.Phil in Machine Learning, Speech & Language Technology
    Oct. 2015 - Sep. 2016, Cambridge, United Kingdom
  • University of Liverpool / B.Eng in Computer Science and Electronic Engineering
    Sep. 2013 - Jun. 2015, Liverpool, United Kingdom
  • Xi'an Jiaotong-Liverpool University / B.Eng in Computer Science & Technology
    Sep. 2011 - Jun. 2013, Suzhou, China

Projects (pre-Ph.D/casual)

  • Mobile Robot Control using ROS (poster)
  • DBD Plasma Reactor Power Monitoring System
  • Wireless Brain-Computer Interface for Game Control (video)
  • FlatShare (code)
  • Gravity Snake (demo, code)