He Zhao

Greetings! This is my research page.

I’m a Senior Research Scientist in machine learning at CSIRO (Australia’s national science agency) and an Adjunct Senior Research Fellow at Monash University. Previously, I obtained my PhD from Monash University and worked as a research fellow there.

I’m interested in developing machine learning methods (e.g., deep learning, probabilistic methods, LLMs) to address problems in science domains.

I serve as an Area Chair for NeurIPS and ICLR and I’m in the editorial board of Machine Learning Journal. My paper won the Best Student Paper Award at KDD 2023.

Selected publications

[NeurIPS 2025, Spotlight, Top 3%] LLM Meeting Decision Trees on Tabular Data
H. Ye, J. Li, H. Zhao, D. Guo, Y. Chang
paper and code coming soon
[ACL 2025, Main Conference] Neural Topic Modeling with Large Language Models in the Loop
X. Yang, H. Zhao, W. Xu, Y. Qi, J. Lu, D. Phung, L. Du
link, code
[ICML 2025, Oral, Top 1%] Rényi Neural Processes
X. Wang, H. Zhao, E. V. Bonilla
link, code
[TACL 2025, Presented at ACL 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
link, code
[CVPR 2025] Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration
J. Ni, H. Zhao, J. Gao, D. Guo, H. Zha
link
[CVPR 2025] FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
C. Shi, H. Zhao, B. Zhang, M. Zhou, D. Guo, Y. Chang
link, code
[CVPR 2025] Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt learning
H. Ren, F. Tang, H. Zhao, Z. Wang, D. Guo, Y. Chang
link
[ICLR 2025] DRL: Decomposed Representation Learning for Tabular Anomaly Detection
H. Ye, H. Zhao, W. Fan, M. Zhou, D. Guo, Y. Chang
link
[ICLR 2025] FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
C. Shi, J. Li, H. Zhao, D. Guo, Y. Chang
link
[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
link, code
[ICML 2024] Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
J. Gao, H. Zhao, D. Guo, H. Zha
link, code
[ICLR 2024, Spotlight, Top 5%] PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
H. Ye, W. Fan, X. Song, S. Zheng, H. Zhao, D. Guo, Y. Chang
link, code
[NeurIPS 2023] Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
M.C. Jung, H. Zhao, J. Dipnall, L. Du
link, code
[NeurIPS 2023] NPCL: Neural Processes for Uncertainty-Aware Continual Learning
S. Jha, D. Gong, H. Zhao, L. Yao
link, code
[NeurIPS 2023] Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification
J. Gao, H. Zhao, Z. Li, D. Guo
link, code
[KDD 2023, 🏆 Best Student Paper] 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, code
[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, Spotlight, Top 5%] Neural Topic Model via Optimal Transport
H. Zhao, D. Phung, V. Huynh, T. Le, W. Buntine
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
[AISTATS 2018] Bayesian multi-label learning with sparse features and labels, and label co-occurrences
H. Zhao, P. Rai, L. Du, W. Buntine
link, code
[ACML 2017] A Word Embeddings Informed Focused Topic Model
H. Zhao, L. Du, W. Buntine
link, code
[ICDM 2017, Long Paper] 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

Senior roles
NeurIPS (Area Chair), ICLR (Area Chair), ACML (Area Chair), Machine Learning Journal (Editorial board member)
Reviewing
NeurIPS (A top 30% reviewer in 2018), ICML, ICLR, …
Tutorials and workshops
Optimal Transport for Unsupervised Learning at Asian Conference on Machine Learning 2021
OT-SDMG: 2025 Workshop on Optimal Transport for Structured Data Modeling and Generation at The Web Conference 2025

Recent Talks

Beyond Predictions: Making AI Reliable with Uncertainty and Causal Understanding
UNSW AI Symposium 2024
link
Enhancing Generalisation and Robustness in Deep Learning with Optimal Transport
School of Computer Science and Engineering, UNSW Sydney, July 2024
slides
Learning Deep Representations with Optimal Transport
Statistics Seminar, The University of Sydney
link