Qiang Liu's Publications

Preprints:

Qiang Liu Page Title

Approximate Inference with Amortised MCMC
Li, Turner, Liu; https://arxiv.org/abs/1702.08343

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
Wang, Liu; https://arxiv.org/pdf/1611.01722

Two Methods for Wild Variational Inference
Liu, Feng; https://arxiv.org/abs/1612.00081

Published:

Qiang Liu Page Title

Stein variational gradient descent as gradient flow
Q. Liu; NIPS 2017

Ultra-Low Power Gaze Tracking for Virtual Reality.
T. Li, Q. Liu, and X. Zhou; SenSys 2017 [video]

Stein Variational Policy Gradient
Y. Liu, Ramachandran, Q. Liu, Peng; UAI 2017

Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Feng, Wang, Liu; UAI 2017

Stein Variational Adaptive Importance Sampling
Han, Q. Liu; UAI 2017

Black-box Importance Sampling
Liu, Lee; AISTATS 2017

Local Perturb-and-MAP for Structured Prediction
Bertasius, Liu, Torresani, Shi; AISTATS 2017

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Liu, Wang; NIPS, 2016. [code]

Bootstrap Model Aggregation for Distributed Statistical Learning
Han, Liu; NIPS, 2016.

Learning Infinite RBMs with Frank-Wolfe
Ping, Liu, Ihler; NIPS, 2016.

Practical Human Sensing in the Light
Li, Liu, Zhou, MobiSys, 2016 [video] [project page] (SIGMobile Research Highlights)

A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation
Liu, Lee, Jordan; ICML, 2016. [code: matlab, R]

Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds
Wang, Fisher, Liu; UAI, 2016.

Importance Weighted Consensus Monte Carlo for Distributed Bayesian Inference
Liu; UAI, 2016.

Communication-Efficient Sparse Regression: a One-Shot Approach
Lee, Liu, Sun, Taylor; JMLR 2016

Probabilistic Variational Bounds for Graphical Models
Liu, Fisher, Ihler; Advances of the Neural Information Processing Systems (NIPS) 2015.

Decomposition Bounds for Marginal MAP
Ping, Liu, Ihler; Advances of the Neural Information Processing Systems (NIPS) 2015.

Estimating the Partition Function by Discriminance Sampling
Liu, Peng, Ihler, Fisher; Uncertainty in Artificial Intelligence (UAI) 2015.

Boosting Crowdsourcing with Expert Labels: Local vs. Global Effects
Liu, Ihler, Fisher; Int'l Conference on Information Fusion 2015.

Distributed Estimation, Information Loss and Exponential Families
Liu, Ihler; Advances in Neural Information Processing Systems (NIPS) 2014.

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy
Zhou, Liu, Platt, Meek; International Conference on Machine Learning (ICML), June 2014. [Code] 

CrowdWiFi: Efficient Crowdsensing of Roadside WiFi Networks
Wu, Liu, Zhang, McCann, Regan, Venkatasubramanian; Middleware' 14.

Marginal structured SVM with hidden variables
Ping, Liu, Ihler; International Conference on Machine Learning (ICML), June 2014.

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
Liu, Ihler, Steyvers; Advances in Neural Information Processing Systems (NIPS) 2013.

Variational Planning for Graph-based MDPs;
Cheng, Liu, Chen, Ihler; Advances in Neural Information Processing Systems (NIPS) 2013.

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] 

Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis;
Geyfman M, Kumar V, Liu Q, Ruiz R, Gordon W, Espitia F, Cam E, Millar SE, Smyth P, Ihler A, Takahashi JS, Andersen B; Proc Natl Acad Sci USA doi:10.1073/pnas.120959210 (2012). 

Belief Propagation for Structured Decision Making;
Liu, Ihler; Uncertainty in Artificial Intelligence (UAI) 2012. [Appendix] 

Distributed Parameter Estimation via Pseudo-likelihood;
Liu, Ihler; International Conference on Machine Learning (ICML) 2012. [Appendix] 

Computational Approaches to Sentence Completion;
Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J.C. Burges, Ainur Yessenalina, and Qiang Liu; in ACL 2012, ACL/SIGPARSE, July 2012.

Variational algorithms for marginal MAP;
Liu, Ihler; Uncertainty in Artificial Intelligence (UAI) 2011. [Full Version] 

Bounding the Partition Function using Holder's Inequality;
Liu, Ihler; International Conference on Machine Learning (ICML) 2011. 

Learning Scale Free Networks by Reweighted l1 Regularization;
Liu, Ihler; AI & Statistics 2010. (notable paper award)

Negative Tree Reweighted Belief Propagation;
Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2010.

Particle Filtered MCMC-MLE with Connections to Contrastive Divergence;
Asuncion, Liu, Ihler, Smyth; Int'l Conf on Machine Learning (ICML), June 2010.

Learning with Blocks: Composite Likelihood and Contrastive Divergence;
Asuncion, Liu, Ihler, Smyth; AI & Statistics (AISTATS), April 2010.

Estimating Replicate Time-Shifts Using Gaussian Process Regression;
Liu, Lin, Anderson, Smyth, Ihler; Bioinformatics 26(6), Mar. 2010, pp. 770-776; doi:10.1093/bioinformatics/btq022.