I'm a PhD student at the University of Toronto, supervised by David Duvenaud. Previously, I had been a MSc student under Mark Schmidt. I was first exposed to research as an undergraduate research assistant for Kevin Leyton-Brown.

I hope to replace black-box deep learning models in favor of more transparent ones, while retaining competitive performance. My recent research is on integrating structured transformations into probabilistic modeling, with the goal of improved interpretability, tractable optimization, or just interestingness. I like to meddle around with stochastic estimation when exact computation is infeasible, enabling information-rich models to be used in larger scopes of applications.

Curriculum Vitae (CV) / Github / Google Scholar

Selected Research

(See CV for full list.)
  • Scalable Gradients for Stochastic Differential Equations Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud International Conference on Artificial Intelligence and Statistics (AISTATS). 2020 arxiv
  • SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models (SPOTLIGHT) Yucen Luo*, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen* International Conference on Learning Representations (ICLR). 2020 openreview
  • Neural Networks with Cheap Differential Operators (SPOTLIGHT) Ricky T. Q. Chen, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | slides | poster
  • Residual Flows for Invertible Generative Modeling (SPOTLIGHT) Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | slides | poster | code
  • Latent ODEs for Irregularly-Sampled Time Series Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | code
  • Invertible Residual Networks (LONG ORAL) Jens Behrmann*, Will Grathwohl*, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen* International Conference on Machine Learning (ICML). 2019 arxiv | bibtex | code
  • FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models (ORAL)
    (BEST STUDENT PAPER @ AABI 2018)
    Will Grathwohl*, Ricky T. Q. Chen*, Jesse Bettencourt, Ilya Sutskever, David Duvenaud International Conference on Learning Representations (ICLR). 2019 arxiv | bibtex | poster | code
  • Neural Ordinary Differential Equations (BEST PAPER AWARD) Ricky T. Q. Chen*, Yulia Rubanova*, Jesse Bettencourt*, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2018 arxiv | bibtex | slides | poster | code
  • Isolating Sources of Disentanglement in Variational Autoencoders (ORAL) Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2018 arxiv | bibtex | slides | poster | code
* denotes equal contribution, often meaning multiple authors contributed to coding and running experiments.