Slides

Stein Variational Method for Deep Probabilistic Modeling, [Project Page, slides v1, slides v2]

Amortized Inference with Implicit Models [slides]

A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation [slides]

Distributed Estimation, Information Loss and Exponential Families [slides]

Belief Propagation for Crowdsourcing [slides]

Reasoning and Decision in Probablistic Grahphical Models - A Unified Approach [slides]

Recent / Upcoming Invited Talks

Simons Institute Workshop: Frontiers of Deep Learning, UC Berkeley, July, 2019

Symposium in Memory of Charles Stein, University of Singapore, June, 2019

SIAM Conference Computer Science Engineering, Spokane, March, 2019

UT Dallas, Computer Science, January, 2019

Allerton Conference, October 2018

Machine Learning Theory Workshop, Peking University, Beijing, China, June 2018

AAAI 2018 Workshop on Planning and Inference, Feb, 2018

SAMSI Workshop on Trends and Advances in Monte Carlo Sampling Algorithms, December, 2017 [link]

Statistics Series, Gatech, September, 2017

Machine learning optimization group lunch, MSR Redmond, August, 2017

ICML 2017 Workshop on Implicit Models, August, 2017 [link]

Conference on Frontiers of Big Data and Statistical Sciences, ICSA Canada Chapter, August, 2017 [link]

11th International Conference on Monte Carlo Methods and Applications, July, 2017 [link]

39th Annual ISMS Marketing Science Conference, USC, June, 2017 [link]

MSR Colloquium, Microsoft Research New England, June, 2017

Machine Learning and Friends Lunch, UMassAmherst, June, 2017

SIAM Conference Computer Science Engineering, Atlanta, March, 2017 [link]

ACDL Seminar Series, MIT, February, 2017

Computer Science, Tsinghua University, December, 2016

NIPS Bayesian Deep Learning Workshop, December, 2016

AI/ML Seminar, Department of Computer Science, UC Irvine, November, 2016

Statistical Seminar, University of Southern California, November, 2016

4th Young Investigator Conference (EITA-YIC), MIT, August, 2015