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’m interested in probabilistic approaches that improve robustness, generalisation, uncertainty estimation, and interpretation of machine/deep learning. I’m also into applications of optimal transport and Bayesian statistics (e.g., probabilistic models and variational inference) in the deep learning context.

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

Selected publications

[ICML 2024] Optimal Transport for Structure Learning Under Missing Data
V. Vo, H. Zhao, T. Le, E. V. Bonilla, D. Phung
[ICML 2024] Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
V. Vo, T. Le, L. T. Vuong, H. Zhao, E. V. Bonilla, D. Phung
[ICML 2024] Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
J. Gao, H. Zhao, D. Guo, H Zha
[ICLR 2024] PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
H. Ye, W. Fan, X. Song, S. Zheng, H. Zhao, D. Guo, Y. Chang
Spotlight, top 5%
[NeurIPS 2023] Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
M.C. Jung, H. Zhao, J. Dipnall, L. Du
[NeurIPS 2023] NPCL: Neural Processes for Uncertainty-Aware Continual Learning
S. Jha, D. Gong, H. Zhao, L. Yao
[NeurIPS 2023] Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification
J. Gao, H. Zhao, Z. Li, D. Guo
[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 5%
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), ACML (Area Chair) …