Hyunjik Kim

Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

I am a third year DPhil student supervised by Prof. Yee Whye Teh. My research interests fall under the topic of scalable probabilistic inference and interpretable machine learning. My current research interests lie in deep generative models and representation learning, especially in using deep generative models to learn disentangled factors of variation in the data. I am also interested in gradient based inference for generative models with discrete units, which ties in closely with interpretability. Previously, I have worked on scaling up inference for Gaussian processes, in particular 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.

Publications

2018

  • H. Kim , A. Mnih , Disentangling by Factorising, 2018.
  • H. Kim , Y. W. Teh , Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes, in Artificial Intelligence and Statistics (AISTATS), 2018.
    Project: bigbayes
  • H. Kim , Y. W. Teh , Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes, in Artificial Intelligence and Statistics (AISTATS), 2018.
    Project: bigbayes

2016

  • H. Kim , X. Lu , S. Flaxman , Y. W. Teh , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 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 , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 2016.