PhD student

Machine Learning Department

School of Computer Science

Carnegie Mellon University

Email: han (DOT) zhao [AT] cs (DOT) cmu (DOT) edu

Office: GHC 8229

[Curriculum Vitae] [Google Scholar]

I am a fourth year PhD student at the Machine Learning Department, Carnegie Mellon University. I am fortunate to work with my advisor, Prof. Geoff Gordon. Before coming to CMU, I obtained my BEng degree from Department of Computer Science and Technology at Tsinghua University and MMath from the University of Waterloo. My research interests include machine learning and optimization, with particular interests in reasoning under uncertainty and inference in probabilistic graphical models. |

Machine Learning Department, Carnegie Mellon University
Sep. 2015 -- Present
PhD student Advisor: Prof. Geoff Gordon |

David R. Cheriton School of Computer Science, University of Waterloo
Sep. 2013 -- May. 2015
Master of Mathematics, Computer Science Advisor: Prof. Pascal Poupart |

Department of Computer Science and Technology, Tsinghua University
Aug. 2009 -- Jul. 2013
Bachelor of Engineering, Computer Science |

David R. Cheriton School of Computer Science, University of Waterloo
Sep. 2012 -- May. 2013
Non-degree Exchange program Advisor: Prof. Pascal Poupart |

Adversarial Multiple Source Domain Adaptation
H. Zhao*, S. Zhang*, G. W, J. Costeira, J. Moura and G. Gordon
In Proceedings of the 32nd Advances in Neural Information Processing Systems ( NIPS 2018)
[abs] [pdf] [supplement] [poster] [code] |

Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
H. Zhao and G. Gordon
In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence ( UAI 2018)
[abs] [pdf] [supplement] |

Convolutional-Recurrent Neural Networks for Speech Enhancement
H. Zhao, S. Zarar, I. Tashev and C. H. Lee
IEEE International Conference on Acoustics, Speech and Signal Processing ( ICASSP 2018)
[abs] [pdf] [slides] |

Linear Time Computation of Moments in Sum-Product Networks
H. Zhao and G. Gordon
In Proceedings of the 31st Advances in Neural Information Processing Systems ( NIPS 2017)
[abs] [pdf] [poster] |

Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption
T. Adel, H. Zhao and A. Wong
In Proceedings of the 31th AAAI Conference on Artificial Intelligence ( AAAI 2017)
[abs] [pdf] |

A Unified Approach for Learning the Parameters of Sum-Product Networks
H. Zhao, P. Poupart and G. Gordon
In Proceedings of the 30th Advances in Neural Information Processing Systems ( NIPS 2016)
[abs] [pdf] [supplement] [poster] [code] |

Online Algorithms for Sum-Product Networks with Continuous Variables
P. Jaini, A. Rashwan, H. Zhao, Y. Liu, E. Banijamali, Z. Chen and P. Poupart
In Proceedings of the 8th International Conference on Probabilistic Graphical Models ( PGM 2016)
[abs] [pdf] |

Collapsed Variational Inference for Sum-Product Networks
H. Zhao, T. Adel, G. Gordon and B. Amos
In Proceedings of the 33rd International Conference on Machine Learning ( ICML 2016)
[abs] [pdf] [poster] [slides] [code] |

Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
A. Rashwan, H. Zhao and P. Poupart
In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics ( AISTATS 2016)
[abs] [pdf] |

On the Relationship between Sum-Product Networks and Bayesian Networks
H. Zhao, M. Melibari and P. Poupart
In Proceedings of the 32nd International Conference on Machine Learning ( ICML 2015)
[abs] [pdf] [supplement] [Full arXiv version] [slides] [poster] |

Self-Adaptive Hierarchical Sentence Model
H. Zhao, Z. Lu and P. Poupart
In Proceedings of the 24th International Joint Conference on Artificial Intelligence ( IJCAI 2015)
[abs] [pdf] [slides] [poster] [code] |

SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering
H. Zhao, P. Pouart, Y. Zhang and M. Lysy
In Proceedings of the 29th AAAI Conference on Artificial Intelligence ( AAAI 2015)
[abs] [pdf] [poster] |

Global Network Alignment in the Context Of Aging
F. Faisal, H. Zhao and T. Milenkovic
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IEEE/ACM TCBB 2014)
In Proceedings of the 4th ACM International Conference on Bioinformatics, Computational Biology and Biomedicine ( ACM BCB 2013)
[abs] [pdf] [supplement] |

On Strategyproof Conference Peer Review
Y. Xu*, H. Zhao*, X. Shi and N. B. Shah
arXiv preprint ( arXiv: 1806.06266)
[abs] [pdf] |

Approximate Empirical Bayes for Deep Neural Networks
H. Zhao*, Y. H. Tsai*, R. Salakhutdinov and G. Gordon
In Uncertainty in Deep Learning workshop at UAI ( UAI UDL 2018)
[abs] [pdf] [slides] [poster] |

Multiple Source Domain Adaptation with Adversarial Learning
H. Zhao*, S. Zhang*, G. W, J. Costeira, J. Moura and G. Gordon
In 6th International Conference on Learning Representations ( ICLR 2018 workshop track)
[abs] [pdf] [poster] |

Discovering Order in Unordered Datasets: Generative Markov Networks
Y. H. Tsai, H. Zhao, R. Salakhutdinov and N. Jojic
In Time Series workshop at NIPS ( NIPS TSW 2017)
[abs] [pdf] [slides] [poster] |

Efficient Multitask Feature and Relationship Learning
H. Zhao O. Stretcu, R. Negrinho, A. Smola and G. Gordon
In Learning with Limited Labeled Data: Weak Supervision and Beyond workshop at NIPS ( NIPS LLD 2017)
[abs] [pdf] [poster] |

A Sober Look at Spectral Learning
H. Zhao and P. Poupart
In Method of Moments and Spectral Learning workshop at ICML ( ICML MM 2014)
[abs] [pdf] [slides] [poster] [code] |

I enjoy sketching and calligraphy at my spare time. If I have a long vacation, I also enjoy traveling.
I enjoy online learning (not the Online Learning in machine learning) and discussing with people from all over the world. Take a look of Coursera and Edx if you are also interested. |