Bayesian Deep Probabilistic Models

I_03_Seungjin Choi(Photo)
Seungjin Choi

Abstract :

Deep generative models are a rich class of models for density estimation which specify a generative process for observed data using a set of stochastic latent variables. Among those, of particular interest is the variational autoencoder (VAE) which is a deep directed generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes (SGVB). In this talk, I begin with introducing our recent elaboration of Gaussian VAE, approximating the local covariance matrix of the decoder as an outer product of the principal direction at a position determined by a sample drawn from Gaussian distribution. I show that this model, referred to as VAE-ROC, better captures the data manifold, compared to the standard Gaussian VAE where independent multivariate Gaussian was used to model the decoder. Then, I introduce an extension of VAE-ROC to handle mixed ordinal and continuous data, where Gaussian copula is adopted to model the local dependency in mixed ordinal and continuous data, leading to “Gaussian copula variational autoencoder”.



  • 1996 Ph.D in Electrical Engineering, University of Notre Dame, Indiana, USA
  • 1989 M.S. in Electrical Engineering, Seoul National University, Korea
  • 1987 B.S. in Electrical Engineering, Seoul National University, Korea


Professional Activities

  • 2016-Present Consulting Professor of Big Data Center of Shinhan Card
  • 2016-Present Consulting Professor of Ministry of the Interior
  • 2014-Present Director of POSTECH Machine Learning Center
  • 2014-Present Founding Chair of Korea Special Interest Group on Machine Learning


Research Interests

  • Bayesian nonparametric models
  • Bayesian learning and inference
  • Deep probabilistic models
  • Random projection
  • Matrix factorization
  • Multi-view learning
  • Multi-task, transfer learning
  • Zero-shot learning
  • Topic models
  • Anomaly detection
  • Recommendation systems


Awards & Honors

  • 2016-2017Senior Program Committee, AAAI
  • 2016 Reviewer, ECCV
  • 2016 Reviewer CVPR
  • 2016 Program Committee, ICML
  • 2015 Area Chair, NIPS
  • 2015 Reviewer, ICCV
  • 2012-2016 Reviewer, AISTATA
  • 2012 Program Co-Chair, SMC
  • 2011-2016 Reviewer, NIPS-2016, NIPS