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

🏆 Best Student Paper - Research, link, code

[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

link, code

[ICML 2023] Vector Quantized Wasserstein Auto-Encoder

L.T. Vuong, T. Le, H. Zhao, C. Zheng, M. Harandi, J. Cai, D. Phung

link, code

[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

link

[NeurIPS 2022] Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

D. Guo, L. Tian, H. Zhao, M. Zhou, H. Zha

link, code

[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

link, code

[ICLR 2022] A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

A. Bui, T. Le, Q. Tran, H. Zhao, D. Phung

link, code

[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

link, code

[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

link

[AISTATS 2022] Particle-based Adversarial Local Distribution Regularization

T. Nguyen, T. Le, H. Zhao, J. Cai, D. Phung

link

[IJCAI 2021 Survey Track] Topic Modelling Meets Deep Neural Networks: A Survey

H. Zhao, D. Phung, V. Huynh, Y. Jin, L. Du, W. Buntine

link, arxiv

[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

link

[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

link, arxiv, code

[AISTATS 2020] Variational Autoencoders for Sparse and Overdispersed Discrete Data

H. Zhao, P. Rai, L. Du, W. Buntine, D. Phung, M. Zhou

link, code

[NeurIPS 2018] Dirichlet Belief Networks for Topic Structure Learning

H. Zhao, L. Du, W. Buntine, M. Zhou

link, code

[ICML 2018] Inter and Intra Topic Structure Learning with Word Embeddings

H. Zhao, L. Du, W. Buntine, M. Zhou

link, code

[ACML 2017] A Word Embeddings Informed Focused Topic Model

H. Zhao, L. Du, W. Buntine

link, code

[ICDM 2017] MetaLDA: A Topic Model that Efficiently Incorporates Meta information

H. Zhao, L. Du, W. Buntine, G. Liu

link, arxiv, code

[ICML 2017] Leveraging Node Attributes for Incomplete Relational Data

H. Zhao, L. Du, W. Buntine

link, arxiv, code

He Zhao

Selected publications

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