Qiang Liu

alt text 

Qiang Liu
Assistant Professor
Computer Science
University of Texas at Austin
Office: GDC 4.806

("Qiang" sounds like "Chee-ah-ng", and "Liu" as "l-yo")


My research area is machine learning and statistics, with interests spreading over the pipeline of data collection (e.g., by crowdsourcing), learning, inference, decision making, and various applications using probabilistic modeling.

Examples of topics of interest: /probabilistic graphical models; variational and Monte Carlo inference; deep learning; neural architecture optimization; energy-efficient learning; deep reinforcement learning; distributed learning; big data problems; kernel and nonparametric methods, etc.

I am an action editor of Journal of Machine Learning Research (JMLR).

Selected / Recent Publications and Slides

<Click for the Full List>

New. Steepest Descent Architecture Optimization: Going Beyond Black Boxes [ paper, paper, slides, poster ]

New. Probabilistic Learning and Inference Using Stein's Method [Project Page, slides v1, slides v2]

Stein Variational Gradient Descent

NeurIPS2016, NeurIPS2017, NeurIPS2018, [code] [more]

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

Liu, Lee, Jordan; ICML, 2016. [code: matlab, R ]

Distributed Estimation, Information Loss and Exponential Families

Liu, Ihler; Advances in Neural Information Processing Systems (NIPS) 2014.

Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework

Liu; PhD Thesis, Fall 2014

Variational Algorithms for Marginal MAP

Liu, Ihler; Journal of Machine Learning Research (JMLR) 2013.

Variational Inference for Crowdsourcing

Liu, Peng, Ihler; Advances in Neural Information Processing Systems (NIPS) 2012. [Appendix, Code]