He Zhao

Greetings! This is my research page.

I’m a machine learning researcher at CSIRO’s Data61 and an adjunct research fellow at Monash University.

I obtained my PhD working with Prof Wray Butine and Dr Lan Du and then did my postdoc with Prof Dinh Phung at Monash University.

I’m interested in probabilistic approaches that improve robustness, generalisation, uncertainty estimation, and interpretation of machine/deep learning. I’m also into optimal transport, Bayesian methods, variational inference, and etc.

🏆 Our paper led by Vy Vo won Best Student Paper - Research at KDD 2023.

Selected publications

[KDD 2023] Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
V. Vo, T. Le, V. Nguyen, H. Zhao, E. Bonilla, G. Haffari, D. Phung
[TMLR 2023] Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
A. Bui, T. Le, H. Zhao, Q.H. Tran, P. Montague, D. Phung
Transactions on Machine Learning Research, link, code
[ICML 2023] Transformed Distribution Matching for Missing Value Imputation
H. Zhao, K. Sun, A. Dezfouli, E. Bonilla
[ICML 2023] Vector Quantized Wasserstein Auto-Encoder
L.T. Vuong, T. Le, H. Zhao, C. Zheng, M. Harandi, J. Cai, D. Phung
[NeurIPS 2022] Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
M.C. Jung, H. Zhao, J. Dipnall, B. Gabbe, L. Du
[NeurIPS 2022] Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
D. Guo, L. Tian, H. Zhao, M. Zhou, H. Zha
[NeurIPS 2022] Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
D. Guo, Z. Li, M. Zheng, H. Zhao, M. Zhou, H. Zha
[ICLR 2022] A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
A. Bui, T. Le, Q. Tran, H. Zhao, D. Phung
[ICLR 2022] Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
D. Wang, D. Guo, H. Zhao, H. Zheng, K. Tanwisuth, B. Chen, M. Zhou
[AISTATS 2022] A Global Defense Approach via Adversarial Attack and Defense Risk Guaranteed Bounds
T. Le, A. Bui, M.T.T. Le, H. Zhao, P. Montague, Q. Tran, D. Phung
[AISTATS 2022] Particle-based Adversarial Local Distribution Regularization
T. Nguyen, T. Le, H. Zhao, J. Cai, D. Phung
[IJCAI 2021 Survey Track] Topic Modelling Meets Deep Neural Networks: A Survey
H. Zhao, D. Phung, V. Huynh, Y. Jin, L. Du, W. Buntine
[ICLR 2021] Neural Topic Model via Optimal Transport
H. Zhao, D. Phung, V. Huynh, T. Le, W. Buntine
Spotlight, top 3% - 7%
link, code
[NeurIPS 2020] OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
V. Huynh, H. Zhao, D. Phung
[ECCV 2020] Improving Adversarial Robustness by Enforcing Local and Global Compactness
A. Bui, T. Le, H. Zhao, P. Montague, O. de Vel, T. Abraham, D. Phung
[AISTATS 2020] Variational Autoencoders for Sparse and Overdispersed Discrete Data
H. Zhao, P. Rai, L. Du, W. Buntine, D. Phung, M. Zhou
[NeurIPS 2018] Dirichlet Belief Networks for Topic Structure Learning
H. Zhao, L. Du, W. Buntine, M. Zhou
[ICML 2018] Inter and Intra Topic Structure Learning with Word Embeddings
H. Zhao, L. Du, W. Buntine, M. Zhou
[ACML 2017] A Word Embeddings Informed Focused Topic Model
H. Zhao, L. Du, W. Buntine
[ICDM 2017] MetaLDA: A Topic Model that Efficiently Incorporates Meta information
H. Zhao, L. Du, W. Buntine, G. Liu
[ICML 2017] Leveraging Node Attributes for Incomplete Relational Data
H. Zhao, L. Du, W. Buntine

Professional Services

NeurIPS (A top 30% reviewer in 2018), ICML, ICLR, JMLR, JAIR, TPAMI, IJCV, TKDE, Machine Learning Journal (Editorial board member), …