He Zhao

Greetings! This is my research page.

I’m a machine learning researcher at CSIRO’s Data61. Before joining CSIRO, I obtained my PhD and worked as a 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.

We are organising OT-SDMG: 2025 Workshop on Optimal Transport for Structured Data Modeling and Generation co-organised with The Web Conference 2025. Please consider submitting your papers!

Selected publications

[TACL 2025] LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models

X. Yang, H. Zhao, D. Phung, W. Buntine, L. Du

Transactions of the Association for Computational Linguistics
paper and code coming soon

[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%
link

[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

🏆 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

[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

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

[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

[AISTATS 2018] Bayesian multi-label learning with sparse features and labels, and label co-occurrences

H. Zhao, P. Rai, L. Du, W. Buntine

[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

link, arxiv, code

[ICML 2017] Leveraging Node Attributes for Incomplete Relational Data

H. Zhao, L. Du, W. Buntine

link, arxiv, code

Professional Services

Reviewing

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

Tutorials

Optimal Transport for Unsupervised Learning at Asian Conference on Machine Learning 2021

Recent Talks

Beyond Predictions: Making AI Reliable with Uncertainty and Causal Understanding

UNSW AI Symposium 2024

Enhancing Generalisation and Robustness in Deep Learning with Optimal Transport

School of Computer Science and Engineering, UNSW Sydney, July 2024

Learning Deep Representations with Optimal Transport

Statistics Seminar, The University of Sydney