I am a graduate student at Montreal Institute for Learning Algorithms (MILA), advised by Aaron Courville. My research is mostly about Deep Latent Variable models and efficient approximate inference. My recent focus is about improving expressivity in doing variational inference (see our ICML18 paper NAF!), the optimization process of inference (our NeuIPS18 paper AVO!), and understanding the training dynamics of generative models in general. I am also interested in meta learning, statistical learning theory and reinforcement learning.

Here’s my one-page CV and google scholar page.

Contact Me






  • Probability Distillation: A Caveat and Alternatives
    • Chin-Wei Huang*, Faruk Ahmed*, Kundan Kumar, Alexandre Lacoste, Aaron Courville
    • to be presented at UAI 2019
  • Hierarchical Importance Weighted Autoencoders [arXiv]
    • Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville
    • presented at ICML 2019
  • Improving Explorability in Variational Inference with Annealed Variational Objectives [arXiv]
    • Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville
    • presented at NIPS 2018
  • Neural Autoregressive Flows [arXiv] [bib] [slides]
    • Chin-Wei Huang*, David Krueger*, Alexandre Lacoste, Aaron Courville
    • presented at ICML 2018 (LONG TALK!)
  • Neural Language Modeling by Jointly Learning Syntax and Lexicon [arXiv] [openreview] [bib]
    • Yikang Shen, Zhouhan Lin, Chin-Wei Huang, Aaron Courville
    • presented at ICLR 2018


  • Generating Contradictory, Neutral, and Entailing Sentences [arXiv]
    • Yikang Shen, Shawn Tan, Chin-Wei Huang, Aaron Courville


  • PAC Bayes Bound Minimization via Normalizing Flows [Generalization, arXiv]
  • Facilitating Multimodality in Normalizing Flows [BDL]
    • Chin-Wei Huang*, David Krueger*, Aaron Courville
    • presented at the NIPS (’17) workshop on Bayesian Deep Learning (BDL)
  • Sequentialized Sampling Importance Resampling and Scalable IWAE [BDL] [bib]
  • Learnable Explicit Density for Continuous Latent Space and Variational Inference [arXiv] [padl] [poster] [bib]
    • Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville
    • presented at the ICML (’17)  workshop on Principle Approaches to Deep Learning (padl)


  • Deconstructive Defense Against Adversarial Attacks [poster]
  • Data Imputation with Latent Variable Models
    • Michal Drozdzal, Mohammad Havaei, Chin-Wei Huang, Laurent Charlin, Nicolas Chapados, Aaron Courville
    • presented in the Montreal AI Symposium (17′)


Technical reports

  • Multilabel Topic Model and User-Item Representation for Personalized Display of Review [report]
    • Chin-Wei Huang, Pierre-André Brousseau
    • A final project report for IFT6266 (Probabilistic Graphical Models, 2016A)



  • Autoregressive Flows for Image Generation and Density Estimation [slides]
    • Presented at 2018 AI Summer School: Vision and Learning @ NTHU
    • Presented at Speech Processing and Machine Learning Lab @ NTU




Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s