Qiang Liu
Associate Professor
Dr. Liu leads the Statistical Learning & AI Group at UT, and has had several recent publications in advanced machine learning. His research group had four papers accepted at this year’s International Conference on Machine Learning, and two papers accepted at the International Conference on Learning Representations.
Research
Research Areas:
Research Interests:
- Probabilistic graphical models
- Variational and Monte Carlo inference
- Deep and distributed learning
- Deep reinforcement learning
- Big data problems
- Kernel and nonparametric methods
- Applications
- crowdsourcing, vision, bioinformatics, etc.
Select Publications
Dilin Wang, Hao Liu, Qiang Liu. 2018. Variational Inference with Tail Adaptive f Divergence . NIPS.
Qiang Liu, Dilin Wang. 2018. Stein Variational Gradient Descent as Moment Matching . NIPS.
Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou. 2018. Breaking the Curse of Horizon: Infinite-Horizon Off-policy Estimation . NIPS.
Jun Han, Qiang Liu. 2018. Stein Variational Gradient Descent Without Gradient . Cornell University.
Tianbing Xu, Liang Zhao, Qiang Liu, Jian Peng. 2018. Learning to Explore via Meta-Policy Gradient . ICML.
