Hyunjik Kim

Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

I am a second year DPhil student supervised by Prof. Yee Whye Teh. My research interests fall under the topic of scalable probabilistic inference. I have worked on regression models for collaborative filtering that are motivated by a scalable approximation to a GP, as well as a method for scaling up the compositional kernel search used by the Automatic Statistician via variational sparse GP methods. I have recently developed an interest in the field of Deep Generative Models, in particular latent variable models whose latent variables are interpretable, for example representing disentangled factors of variation in the data.

Publications

2016

  • H. Kim, Y. W. Teh, Scalable Structure Discovery in Regression using Gaussian Processes, in Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016.
    Project: bigbayes
  • H. Kim, X. Lu, S. Flaxman, Y. W. Teh, Tucker Gaussian Process for Regression and Collaborative Filtering, 2016.
    Project: bigbayes

2005

  • H. Kim, B. K. Mallick, C. Holmes, Analyzing nonstationary spatial data using piecewise Gaussian processes, Journal of the American Statistical Association, vol. 100, no. 470, 653–668, 2005.