Han Zhao | 赵晗


PhD student
Machine Learning Department
School of Computer Science
Carnegie Mellon University
Email: han (DOT) zhao [AT] cs (DOT) cmu (DOT) edu
Office: GHC 8021
[Curriculum Vitae] [Google Scholar] [Research Statement]

Bio

I am a final-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 the Computer Science Department at Tsinghua University and MMath from the University of Waterloo. I have a broad interest in both the theoretical and applied side of machine learning. In particular, I work on invariant representation learning, probabilistic reasoning with Sum-Product Networks, transfer and multitask learning, and computational social choice.

I also co-organize the AI seminar @ CMU, feel free to drop me an email if you are interested to give a talk!

Publications [ show selected / show by date / show by topic ]

Invariant Representation Learning

My work proves the fundamental limit of learning invariant representations. With this result, we also identify and explain the inherent trade-offs in unsupervised domain adaptation and algorithmic fairness. Selected papers include:
Conditional Learning of Fair Representations
H. Zhao, A. Coston, T. Adel and G. Gordon
In Proceedings of the 8th International Conference on Learning Representations (ICLR 2020, Spotlight)
[abs] [pdf] [poster] [slides]
Inherent Tradeoffs in Learning Fair Representations
H. Zhao and G. Gordon
In Proceedings of the 33rd Advances in Neural Information Processing Systems (NeurIPS 2019)
[abs] [pdf] [poster] [slides]
On Learning Invariant Representations for Domain Adaptation
H. Zhao, R. Tachet, K. Zhang and G. Gordon
In Proceedings of the 36th International Conference on Machine Learning (ICML 2019, Long Oral)
[abs] [pdf] [supplement] [poster] [slides] [blog]
Adversarial Multiple Source Domain Adaptation
H. Zhao*, S. Zhang*, G. Wu, J. Costeira, J. Moura and G. Gordon
In Proceedings of the 32nd Advances in Neural Information Processing Systems (NeurIPS 2018)
[abs] [pdf] [supplement] [poster] [code]

Tractable Probabilistic Reasoning

My work establishes the equivalence between Sum-Product networks, Bayesian networks with algebraic decision diagrams, and mixture models with exponentially many components. With these theoretical results, we propose efficient learning algorithms for Sum-Product networks in both offline and online, distributed and Bayesian settings. Selected papers include:
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]
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]
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]

Workshop Papers and Pre-prints

Conditional Learning of Fair Representations
H. Zhao, A. Coston, T. Adel and G. Gordon
NeurIPS 2019 Workshop on Machine Learning with Guarantees (NeurIPS 2019)
[abs] [pdf]
Adversarial Privacy Preservation under Attribute Inference Attack
H. Zhao*, J. Chi*, Y. Tian and G. Gordon
NeurIPS 2019 Workshop on Machine Learning with Guarantees (NeurIPS 2019)
[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] [poster]
Multiple Source Domain Adaptation with Adversarial Learning
H. Zhao*, S. Zhang*, G. Wu, 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]
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]

Misc

I enjoy sketching and calligraphy at my spare time. If I have a long vacation, I also enjoy traveling.