A Word Embeddings Informed Focused Topic Model. - He Zhao 001; Lan Du 002; Wray L. Buntine (ACML, 2017)

MetaLDA: A Topic Model that Efficiently Incorporates Meta Information. - He Zhao 001; Lan Du 002; Wray L. Buntine; Gang Liu 021 (ICDM, 2017)

Leveraging Node Attributes for Incomplete Relational Data. - He Zhao 001; Lan Du 002; Wray L. Buntine (ICML, 2017)

Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences. - He Zhao 001; Piyush Rai; Lan Du 002; Wray L. Buntine (AISTATS, 2018)

Inter and Intra Topic Structure Learning with Word Embeddings. - He Zhao 001; Lan Du 002; Wray L. Buntine; Mingyuan Zhou (ICML, 2018)

Dirichlet belief networks for topic structure learning. - He Zhao 001; Lan Du 002; Wray L. Buntine; Mingyuan Zhou (NeurIPS, 2018)

Leveraging Meta Information in Short Text Aggregation. - He Zhao 001; Lan Du 002; Guanfeng Liu 001; Wray L. Buntine (ACL (1), 2019)

Variational Autoencoders for Sparse and Overdispersed Discrete Data. - He Zhao 001; Piyush Rai; Lan Du 002; Wray L. Buntine; Mingyuan Zhou (CoRR, 2019)

Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. - He Zhao 001; Trung Le 001; Paul Montague; Olivier Y. de Vel; Tamas Abraham; Dinh Phung 001 (CoRR, 2019)

Leveraging external information in topic modelling. - He Zhao 001; Lan Du 002; Wray L. Buntine; Gang Liu 021 (Knowl. Inf. Syst., 2019)

Structured Bayesian Latent Factor Models with Meta-data. - He Zhao 001 (2019)

Variational Autoencoders for Sparse and Overdispersed Discrete Data. - He Zhao 001; Piyush Rai; Lan Du 002; Wray L. Buntine; Dinh Phung 001; Mingyuan Zhou (AISTATS, 2020)

Improving Adversarial Robustness by Enforcing Local and Global Compactness. - Tuan-Anh Bui; Trung Le 001; He Zhao 001; Paul Montague; Olivier Y. DeVel; Tamas Abraham; Dinh Q. Phung (ECCV (27), 2020)

Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. - Mahmoud Hossam; Trung Le 001; He Zhao 001; Dinh Phung 001 (ICPR, 2020)

OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. - Viet Huynh; He Zhao 001; Dinh Phung 001 (NeurIPS, 2020)

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression. - Jinming Zhao; Ming Liu 028; Longxiang Gao; Yuan Jin; Lan Du 002; He Zhao 001; He Zhang 012; Gholamreza Haffari (SIGIR, 2020)

Leveraging Cross Feedback of User and Item Embeddings for Variational Autoencoder based Collaborative Filtering. - Yuan Jin; He Zhao 001; Ming Liu 028; Lan Du 002; Yunfeng Li; Ruohua Xu; Longxiang Gao (CoRR, 2020)

MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models. - Thanh Nguyen-Duc; He Zhao 001; Jianfei Cai 001; Dinh Phung 001 (CoRR, 2020)

Neural Sinkhorn Topic Model. - He Zhao 001; Dinh Phung 001; Viet Huynh; Trung Le 001; Wray L. Buntine (CoRR, 2020)

Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images. - He Zhao 001; Trung Le 001; Paul Montague; Olivier Y. de Vel; Tamas Abraham; Dinh Phung 001 (CoRR, 2020)

Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. - Tuan-Anh Bui; Trung Le 001; He Zhao 001; Paul Montague; Olivier Y. de Vel; Tamas Abraham; Dinh Phung 001 (AAAI, 2021)

Neural Attention-Aware Hierarchical Topic Model. - Yuan Jin; He Zhao 001; Ming Liu 028; Lan Du 002; Wray L. Buntine (EMNLP (1), 2021)

Neural Topic Model via Optimal Transport. - He Zhao 001; Dinh Phung 001; Viet Huynh; Trung Le 001; Wray L. Buntine (ICLR, 2021)

Optimal Transport for Deep Generative Models: State of the Art and Research Challenges. - Viet Huynh; Dinh Q. Phung; He Zhao 001 (IJCAI, 2021)

Topic Modelling Meets Deep Neural Networks: A Survey. - He Zhao 001; Dinh Q. Phung; Viet Huynh; Yuan Jin; Lan Du 002; Wray L. Buntine (IJCAI, 2021)

Most: multi-source domain adaptation via optimal transport for student-teacher learning. - Tuan Nguyen 004; Trung Le 001; He Zhao 001; Quan Hung Tran; Truyen Nguyen; Dinh Q. Phung (UAI, 2021)

Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning. - Tuan-Anh Bui; Trung Le 001; He Zhao 001; Paul Montague; Seyit Camtepe; Dinh Phung 001 (CoRR, 2021)

Improved and Efficient Text Adversarial Attacks using Target Information. - Mahmoud Hossam; Trung Le 001; He Zhao 001; Viet Huynh; Dinh Phung 001 (CoRR, 2021)

Multi-Label Image Classification with Contrastive Learning. - Son Duy Dao; Ethan Zhao 001; Dinh Phung 001; Jianfei Cai 001 (CoRR, 2021)

On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. - Trung Le 001; Anh Tuan Bui; Le Minh Tri Tue; He Zhao 001; Paul Montague; Quan Hung Tran; Dinh Q. Phung (AISTATS, 2022)

Particle-based Adversarial Local Distribution Regularization. - Thanh Nguyen-Duc; Trung Le 001; He Zhao 001; Jianfei Cai 001; Dinh Q. Phung (AISTATS, 2022)

Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings. - Dongsheng Wang 003; Dandan Guo; He Zhao 001; Huangjie Zheng; Korawat Tanwisuth; Bo Chen 001; Mingyuan Zhou (ICLR, 2022)

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. - Anh Tuan Bui; Trung Le 001; Quan Hung Tran; He Zhao 001; Dinh Q. Phung (ICLR, 2022)

MED-TEX: Transfer and Explain Knowledge with Less Data from Pretrained Medical Imaging Models. - Thanh Nguyen-Duc; He Zhao 001; Jianfei Cai 001; Dinh Phung 001 (ISBI, 2022)

A Label-Aware Autoregressive Framework for Cross-Domain NER. - Jinpeng Hu; He Zhao 001; Dan Guo; Xiang Wan; Tsung-Hui Chang (NAACL-HLT (Findings), 2022)

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification. - Dandan Guo; Zhuo Li; Meixi Zheng; He Zhao 001; Mingyuan Zhou; Hongyuan Zha (NeurIPS, 2022)

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport. - Dandan Guo; Long Tian; He Zhao 001; Mingyuan Zhou; Hongyuan Zha (NeurIPS, 2022)

Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture. - Myong Chol Jung; He Zhao 001; Joanna Dipnall; Belinda Gabbe; Lan Du 002 (NeurIPS, 2022)

Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation. - Tuan Nguyen 004; Van Nguyen 002; Trung Le 001; He Zhao 001; Quan Hung Tran; Dinh Q. Phung (UAI, 2022)

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. - Tuan-Anh Bui; Trung Le 001; Quan Hung Tran; He Zhao 001; Dinh Q. Phung (CoRR, 2022)

Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations. - Vy Vo; Trung Le 001; Van Nguyen 002; He Zhao 001; Edwin V. Bonilla; Gholamreza Haffari; Dinh Q. Phung (CoRR, 2022)

Open-Vocabulary Multi-label Image Classification with Pretrained Vision-Language Model. - Son Duy Dao; Dat Huynh; He Zhao 001; Dinh Phung 001; Jianfei Cai 001 (ICME, 2023)

Transformed Distribution Matching for Missing Value Imputation. - He Zhao 001; Ke Sun 001; Amir Dezfouli; Edwin V. Bonilla (ICML, 2023)

Vector Quantized Wasserstein Auto-Encoder. - Long Tung Vuong; Trung Le 001; He Zhao 001; Chuanxia Zheng; Mehrtash Harandi; Jianfei Cai 001; Dinh Q. Phung (ICML, 2023)

Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. - Vy Vo; Trung Le 001; Van Nguyen 002; He Zhao 001; Edwin V. Bonilla; Gholamreza Haffari; Dinh Q. Phung (KDD, 2023)

Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation. - Thanh Nguyen-Duc; Trung Le 001; Roland Bammer; He Zhao 001; Jianfei Cai 001; Dinh Q. Phung (MICCAI (1), 2023)

Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification. - Jintong Gao; He Zhao 001; Zhuo Li; Dandan Guo (NeurIPS, 2023)

NPCL: Neural Processes for Uncertainty-Aware Continual Learning. - Saurav Jha; Dong Gong; He Zhao 001; Lina Yao 001 (NeurIPS, 2023)

Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation. - Myong Chol Jung; He Zhao 001; Joanna Dipnall; Lan Du 002 (NeurIPS, 2023)

Adversarial local distribution regularization for knowledge distillation. - Thanh Nguyen-Duc; Trung Le 001; He Zhao 001; Jianfei Cai 001; Dinh Phung 001 (WACV, 2023)

Vector Quantized Wasserstein Auto-Encoder. - Tung-Long Vuong; Trung Le 001; He Zhao 001; Chuanxia Zheng; Mehrtash Harandi; Jianfei Cai 001; Dinh Q. Phung (CoRR, 2023)

Multimodal Neural Processes for Uncertainty Estimation. - Myong Chol Jung; He Zhao 001; Joanna Dipnall; Belinda Gabbe; Lan Du 002 (CoRR, 2023)

Learning Directed Graphical Models with Optimal Transport. - Vy Vo; Trung Le 001; Long Tung Vuong; He Zhao 001; Edwin V. Bonilla; Dinh Q. Phung (CoRR, 2023)

Towards Generalising Neural Topical Representations. - Xiaohao Yang; He Zhao 001; Dinh Phung 001; Lan Du 002 (CoRR, 2023)

Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. - Anh Tuan Bui; Trung Le 001; He Zhao 001; Quan Hung Tran; Paul Montague; Dinh Phung 001 (Trans. Mach. Learn. Res., 2023)

Contrastively enforcing distinctiveness for multi-label image classification. - Son Duy Dao; He Zhao 001; Dinh Q. Phung; Jianfei Cai 001 (Neurocomputing, 2023)

PTaRL: Prototype-based Tabular Representation Learning via Space Calibration. - Hangting Ye; Wei Fan 010; Xiaozhuang Song; Shun Zheng 001; He Zhao 001; Dandan Guo; Yi Chang 001 (ICLR, 2024)

Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification. - Jintong Gao; He Zhao 001; Dandan Guo; Hongyuan Zha (ICML, 2024)

Optimal Transport for Structure Learning Under Missing Data. - Vy Vo; He Zhao 001; Trung Le 001; Edwin V. Bonilla; Dinh Phung 001 (ICML, 2024)

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport. - Vy Vo; Trung Le 001; Long Tung Vuong; He Zhao 001; Edwin V. Bonilla; Dinh Phung 001 (ICML, 2024)

Neural Topic Model with Distance Awareness. - Shangyu Chen; He Zhao 001; Viet H. Huynh; Dinh Q. Phung; Jianfei Cai 001 (ICPR (9), 2024)

A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation. - Tuan Nguyen 004; Van Nguyen 002; Trung Le 001; He Zhao 001; Quan Hung Tran; Dinh Q. Phung (CoRR, 2024)

Variational DAG Estimation via State Augmentation With Stochastic Permutations. - Edwin V. Bonilla; Pantelis Elinas; He Zhao 001; Maurizio Filippone; Vassili Kitsios; Terry O'Kane (CoRR, 2024)

Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data. - He Zhao 001; Edwin V. Bonilla (CoRR, 2024)

Extracting Clean and Balanced Subset for Noisy Long-tailed Classification. - Zhuo Li; He Zhao 001; Zhen Li 026; Tongliang Liu; Dandan Guo; Xiang Wan (CoRR, 2024)

Rényi Neural Processes. - Xuesong Wang 002; He Zhao 001; Edwin V. Bonilla (CoRR, 2024)

Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs. - Myong Chol Jung; He Zhao 001; Joanna Dipnall; Belinda Gabbe; Lan Du 002 (CoRR, 2024)

Neural Topic Modeling with Large Language Models in the Loop. - Xiaohao Yang; He Zhao 001; Weijie Xu; Yuanyuan Qi; Jueqing Lu; Dinh Phung 001; Lan Du 002 (ACL (1), 2025)

Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration. - Jiani Ni; He Zhao 001; Jintong Gao; Dandan Guo; Hongyuan Zha (CVPR, 2025)

Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning. - Hairui Ren; Fan Tang; He Zhao 001; Zixuan Wang; Dandan Guo; Yi Chang 001 (CVPR, 2025)

FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors. - Changlong Shi; He Zhao 001; Bingjie Zhang 009; Mingyuan Zhou; Dandan Guo; Yi Chang 001 (CVPR, 2025)

FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking. - Changlong Shi; Jinmeng Li; He Zhao 001; Dandan Guo; Yi Chang 001 (ICLR, 2025)

DRL: Decomposed Representation Learning for Tabular Anomaly Detection. - Hangting Ye; He Zhao 001; Wei Fan 010; Mingyuan Zhou; Dandan Guo; Yi Chang 001 (ICLR, 2025)

LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models. - Xiaohao Yang; He Zhao 001; Dinh Q. Phung; Wray L. Buntine; Lan Du 002 (Trans. Assoc. Comput. Linguistics, 2025)

Diverse Condensed Data Generation via Class Preserving Distribution Matching. - Dandan Guo; Zhuo Li; He Zhao 001; Mingyuan Zhou; Hongyuan Zha (Trans. Mach. Learn. Res., 2025)

Synthesizing Minority Samples for Long-tailed Classification via Distribution Matching. - Zhuo Li; He Zhao 001; Jinke Ren; Anningzhe Gao; Dandan Guo; Xiang Wan; Hongyuan Zha (Trans. Mach. Learn. Res., 2025)

Modality-Consistent Prompt Tuning With Optimal Transport. - Hairui Ren; Fan Tang; Huangjie Zheng; He Zhao 001; Dandan Guo; Yi Chang 001 (IEEE Trans. Circuits Syst. Video Technol., 2025)

Direct Advantage Regression: Aligning LLMs with Online AI Reward. - Li He; He Zhao 001; Stephen Wan 001; Dadong Wang; Lina Yao 001; Tongliang Liu (CoRR, 2025)

LLM Meeting Decision Trees on Tabular Data. - Hangting Ye; Jinmeng Li; He Zhao 001; Dandan Guo; Yi Chang 001 (CoRR, 2025)

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data. - Bingjie Zhang 009; Hongkang Li; Changlong Shi; Guowei Rong; He Zhao 001; Dongsheng Wang 003; Dandan Guo; Meng Wang (CoRR, 2025)

Deep Neural Network Calibration by Reducing Classifier Shift with Stochastic Masking. - Jiani Ni; He Zhao 001; Yibo Yang; Dandan Guo (CoRR, 2025)

LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data. - Peng Wang; Dongsheng Wang; He Zhao 001; Hangting Ye; Dandan Guo; Yi Chang 001 (CoRR, 2025)

Prototype-Oriented Clean Subset Extraction for Noisy Long-Tailed Classification. - Zhuo Li; He Zhao 001; Anningzhe Gao; Dandan Guo; Tsung-Hui Chang; Xiang Wan (IEEE Trans. Circuits Syst. Video Technol., 2025)