| 2026 | ACL | LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations. | Viet Thanh Pham, Lizhen Qu, Thuy-Trang Vu, Gholamreza Haffari, Dinh Q. Phung |
| 2025 | CVPR | Enhancing Dataset Distillation via Non-Critical Region Refinement. | Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do, Dinh Q. Phung |
| 2025 | CVPR | PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting. | Cheng Zhang, Haofei Xu, Qianyi Wu, Camilo Cruz Gambardella, Dinh Q. Phung, Jianfei Cai |
| 2025 | ICCV | Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective. | Hoang Phan, Lam Tran, Quyen Tran, Ngoc N. Tran, Tuan Truong, Qi Lei, Nhat Ho, Dinh Q. Phung, Trung Le |
| 2025 | ICLR | PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction. | Shangyu Chen, Zizheng Pan, Jianfei Cai, Dinh Q. Phung |
| 2025 | ICLR | Boosting Multiple Views for pretrained-based Continual Learning. | Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Dinh Q. Phung, Khoat Than, Trung Le |
| 2024 | CVPR | NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation. | Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung Tran, Dinh Q. Phung |
| 2024 | ICPR | Stereographic Projection for Embedding Hierarchical Structures in Hyperbolic Space. | Shangyu Chen, Xiaohao Yang, Pengfei Fang, Mehrtash Tafazzoli Harandi, Dinh Q. Phung, Jianfei Cai |
| 2024 | ICPR | Neural Topic Model with Distance Awareness. | Shangyu Chen, He Zhao, Viet H. Huynh, Dinh Q. Phung, Jianfei Cai |
| 2024 | WACV | Frequency Attention for Knowledge Distillation. | Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do |
| 2023 | AISTATS | Global-Local Regularization Via Distributional Robustness. | Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung |
| 2023 | EMNLP | Systematic Assessment of Factual Knowledge in Large Language Models. | Linhao Luo, Thuy-Trang Vu, Dinh Q. Phung, Gholamreza Haffari |
| 2023 | ICASSP | On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks. | Dang Nguyen, Trang Nguyen, Khai Nguyen, Dinh Q. Phung, Hung Hai Bui, Nhat Ho |
| 2023 | ICML | Vector Quantized Wasserstein Auto-Encoder. | Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung |
| 2023 | KDD | Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. | Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung |
| 2023 | MICCAI | Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation. | Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Q. Phung |
| 2022 | ACL | Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training. | Thuy-Trang Vu, Shahram Khadivi, Dinh Q. Phung, Gholamreza Haffari |
| 2022 | AISTATS | On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. | Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung |
| 2022 | AISTATS | Particle-based Adversarial Local Distribution Regularization. | Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung |
| 2022 | CVPR | Bridging Global Context Interactions for High-Fidelity Image Completion. | Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai, Dinh Q. Phung |
| 2022 | ICLR | A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. | Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung |
| 2022 | Interspeech | A Vietnamese-English Neural Machine Translation System. | Tuan-Duy H. Nguyen, Duy Phung, Duy Tran-Cong Nguyen, Hieu Minh Tran, Manh Luong, Tin Duy Vo, Hung Hai Bui, Dinh Q. Phung, Dat Quoc Nguyen |
| 2022 | IUI | Vietnamese Speech-based Question Answering over Car Manuals. | Tin Duy Vo, Manh Luong, Duong Minh Le, Hieu Tran, Nhan Do, Tuan-Duy H. Nguyen, Thien Nguyen, Hung Bui, Dat Quoc Nguyen, Dinh Q. Phung |
| 2022 | WWW | Universal Graph Transformer Self-Attention Networks. | Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung |
| 2022 | WWW | QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. | Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Q. Phung |
| 2022 | UAI | Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation. | Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung |
| 2022 | WSDM | Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction. | Dai Quoc Nguyen, Vinh Tong, Dinh Q. Phung, Dat Quoc Nguyen |
| 2021 | ACML | Quaternion Graph Neural Networks. | Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung |
| 2021 | EMNLP | Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection. | Thuy-Trang Vu, Xuanli He, Dinh Q. Phung, Gholamreza Haffari |
| 2021 | IJCAI | Optimal Transport for Deep Generative Models: State of the Art and Research Challenges. | Viet Huynh, Dinh Q. Phung, He Zhao |
| 2021 | IJCAI | TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport. | Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung |
| 2021 | IJCAI | Topic Modelling Meets Deep Neural Networks: A Survey. | He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine |
| 2021 | UAI | Most: multi-source domain adaptation via optimal transport for student-teacher learning. | Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung |
| 2020 | ECCV | Improving Adversarial Robustness by Enforcing Local and Global Compactness. | Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Q. Phung |
| 2019 | AAAI | Robust Anomaly Detection in Videos Using Multilevel Representations. | Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung |
| 2019 | AISTATS | Probabilistic Multilevel Clustering via Composite Transportation Distance. | Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan |
| 2019 | ICLR | Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. | Tue Le, Tuan Nguyen, Trung Le, Dinh Q. Phung, Paul Montague, Olivier Y. de Vel, Lizhen Qu |
| 2019 | IJCAI | Learning Generative Adversarial Networks from Multiple Data Sources. | Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung |
| 2019 | IJCNN | Deep Domain Adaptation for Vulnerable Code Function Identification. | Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. DeVel, Paul Montague, Lizhen Qu, Dinh Q. Phung |
| 2019 | NAACL | A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization. | Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung |
| 2018 | ACML | Clustering Induced Kernel Learning. | Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung |
| 2018 | ACML | Batch Normalized Deep Boltzmann Machines. | Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung |
| 2018 | ICLR | MGAN: Training Generative Adversarial Nets with Multiple Generators. | Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung |
| 2018 | ICPR | Bayesian Multi-Hyperplane Machine for Pattern Recognition. | Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung |
| 2018 | IJCAI | Geometric Enclosing Networks. | Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung |
| 2018 | KDD | Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. | Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb |
| 2018 | NAACL | A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. | Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung |
| 2018 | WISE | Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. | Hung Nguyen, Van Nguyen, Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Duc Thanh Nguyen, Trung Le, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen |
| 2018 | SDM | Learning Graph Representation via Frequent Subgraphs. | Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2017 | AAAI | Column Networks for Collective Classification. | Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2017 | DSAA | Forward-Backward Smoothing for Hidden Markov Models of Point Pattern Data. | Nhan Dam, Dinh Q. Phung, Ba-Ngu Vo, Viet Huynh |
| 2017 | DSAA | Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring. | Hung Nguyen, Sarah J. Maclagan, Tu Dinh Nguyen, Thin Nguyen, Paul Flemons, Kylie Andrews, Euan G. Ritchie, Dinh Q. Phung |
| 2017 | ICDM | GoGP: Fast Online Regression with Gaussian Processes. | Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung |
| 2017 | ICML | Multilevel Clustering via Wasserstein Means. | Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung |
| 2017 | IJCAI | Large-scale Online Kernel Learning with Random Feature Reparameterization. | Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung |
| 2017 | IJCAI | Discriminative Bayesian Nonparametric Clustering. | Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui |
| 2017 | PAKDD | Energy-Based Localized Anomaly Detection in Video Surveillance. | Hung Vu, Tu Dinh Nguyen, Anthony Travers, Svetha Venkatesh, Dinh Q. Phung |
| 2017 | WWW | Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features. | Thin Nguyen, Duc Thanh Nguyen, Mark E. Larsen, Bridianne O'Dea, John Yearwood, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen |
| 2017 | UAI | Supervised Restricted Boltzmann Machines. | Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le |
| 2017 | WISE | Estimating Support Scores of Autism Communities in Large-Scale Web Information Systems. | Thin Nguyen, Nguyen Hung, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | ACML | Multiple Kernel Learning with Data Augmentation. | Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung |
| 2016 | ADMA | Outlier Detection on Mixed-Type Data: An Energy-Based Approach. | Kien Do, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | ADMA | Stabilizing Linear Prediction Models Using Autoencoder. | Shivapratap Gopakumar, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | ADMA | Textual Cues for Online Depression in Community and Personal Settings. | Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | AISTATS | Nonparametric Budgeted Stochastic Gradient Descent. | Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung |
| 2016 | DSAA | Analysing the History of Autism Spectrum Disorder Using Topic Models. | Adham Beykikhoshk, Dinh Q. Phung, Ognjen Arandjelovic, Svetha Venkatesh |
| 2016 | DSAA | Learning Multifaceted Latent Activities from Heterogeneous Mobile Data. | Nguyen Thanh Binh, Vu Nguyen, Nguyen Cong Thuong, Svetha Venkatesh, Mohan Kumar, Dinh Q. Phung |
| 2016 | ICDM | One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems. | Vu Nguyen, Tu Dinh Nguyen, Trung Le, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | ICPR | Stable clinical prediction using graph support vector machines. | Iman Kamkar, Sunil Gupta, Cheng Li, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | ICPR | Distributed data augmented support vector machine on Spark. | Tu Dinh Nguyen, Vu Nguyen, Trung Le, Dinh Q. Phung |
| 2016 | ICPR | MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data. | Nguyen Thanh Binh, Vu Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | ICPR | Faster training of very deep networks via p-norm gates. | Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | ICPR | Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model. | Budhaditya Saha, Sunil Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | ICPR | Clustering for point pattern data. | Nhat-Quang Tran, Ba-Ngu Vo, Dinh Q. Phung, Ba-Tuong Vo |
| 2016 | ICPR | Model-based classification and novelty detection for point pattern data. | Ba-Ngu Vo, Nhat-Quang Tran, Dinh Q. Phung, Ba-Tuong Vo |
| 2016 | OZCHI | Computer assisted autism interventions for India. | Pratibha Vellanki, Stewart Greenhill, Thi V. Duong, Dinh Q. Phung, Svetha Venkatesh, Jayashree Godwin, Krishnaveni Achary, Blessin Varkey |
| 2016 | PAKDD | Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework. | Cheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashely, Dinh Q. Phung |
| 2016 | PAKDD | Sparse Adaptive Multi-hyperplane Machine. | Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Q. Phung |
| 2016 | PAKDD | Learning Multi-faceted Activities from Heterogeneous Data with the Product Space Hierarchical Dirichlet Processes. | Nguyen Thanh Binh, Vu Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | PAKDD | DeepCare: A Deep Dynamic Memory Model for Predictive Medicine. | Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | PAKDD | Neural Choice by Elimination via Highway Networks. | Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2016 | PERCOM | SECC: Simultaneous extraction of context and community from pervasive signals. | Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Dinh Q. Phung |
| 2016 | UAI | Scalable Nonparametric Bayesian Multilevel Clustering. | Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui |
| 2016 | UAI | Budgeted Semi-supervised Support Vector Machine . | Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung |
| 2016 | WISE | Discriminative Cues for Different Stages of Smoking Cessation in Online Community. | Thin Nguyen, Ron Borland, John Yearwood, Hua-Hie Yong, Svetha Venkatesh, Dinh Q. Phung |
| 2016 | WISE | Large-Scale Stylistic Analysis of Formality in Academia and Social Media. | Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2015 | AAAI | Tensor-Variate Restricted Boltzmann Machines. | Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | ACML | Streaming Variational Inference for Dirichlet Process Mixtures. | Viet Huynh, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | DICTA | Multi-View Subspace Clustering for Face Images. | Xin Zhang, Dinh Q. Phung, Svetha Venkatesh, Duc-Son Pham, Wanquan Liu |
| 2015 | DSAA | Nonparametric discovery of online mental health-related communities. | Bo Dao, Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2015 | DSAA | Exploiting feature relationships towards stable feature selection. | Iman Kamkar, Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | PAKDD | Hierarchical Dirichlet Process for Tracking Complex Topical Structure Evolution and Its Application to Autism Research Literature. | Adham Beykikhoshk, Ognjen Arandjelovic, Svetha Venkatesh, Dinh Q. Phung |
| 2015 | PAKDD | Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records. | Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | PAKDD | Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning. | Sunil Kumar Gupta, Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | PAKDD | Learning Conditional Latent Structures from Multiple Data Sources. | Viet Huynh, Dinh Q. Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui |
| 2015 | PAKDD | Fast One-Class Support Vector Machine for Novelty Detection. | Trung Le, Dinh Q. Phung, Khanh Nguyen, Svetha Venkatesh |
| 2015 | PAKDD | Small-Variance Asymptotics for Bayesian Nonparametric Models with Constraints. | Cheng Li, Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2015 | PAKDD | A Bayesian Nonparametric Approach to Multilevel Regression. | Vu Nguyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui |
| 2015 | PAKDD | Learning Entry Profiles of Children with Autism from Multivariate Treatment Information Using Restricted Boltzmann Machines. | Pratibha Vellanki, Dinh Q. Phung, Thi V. Duong, Svetha Venkatesh |
| 2015 | WACV | Visual Object Clustering via Mixed-Norm Regularization. | Xin Zhang, Duc-Son Pham, Dinh Q. Phung, Wanquan Liu, Budhaditya Saha, Svetha Venkatesh |
| 2015 | WISE | Differentiating Sub-groups of Online Depression-Related Communities Using Textual Cues. | Thin Nguyen, Bridianne O'Dea, Mark E. Larsen, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen |
| 2015 | SDM | What shall I share and with Whom? - A Multi-Task Learning Formulation using Multi-Faceted Task Relationships. | Sunil Gupta, Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2014 | ACML | Preface. | Dinh Q. Phung, Hang Li |
| 2014 | DSAA | Analysis of circadian rhythms from online communities of individuals with affective disorders. | Bo Dao, Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | DSAA | Individualized arrhythmia detection with ECG signals from wearable devices. | Nguyen Thanh Binh, Wei Luo, Terry Caelli, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | ESANN | Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. | Dinh Q. Phung, Dat Tran, Wanli Ma, Phuoc Nguyen, Tien Pham |
| 2014 | FUSION | A random finite set model for data clustering. | Dinh Q. Phung, Ba-Ngu Vo |
| 2014 | ICPR | Regularizing Topic Discovery in EMRs with Side Information by Using Hierarchical Bayesian Models. | Cheng Li, Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2014 | ICPR | A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data. | Nguyen Cong Thuong, Sunil Gupta, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | ICPR | Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data. | Pratibha Vellanki, Thi V. Duong, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | IJCNN | Multi-factor EEG-based user authentication. | Tien Pham, Wanli Ma, Dat Tran, Phuoc Nguyen, Dinh Q. Phung |
| 2014 | IJCNN | Investigating the impacts of epilepsy on EEG-based person identification systems. | Dinh Q. Phung, Dat Tran, Wanli Ma, Phuoc Nguyen, Tien Pham |
| 2014 | MUM | Unsupervised inference of significant locations from WiFi data for understanding human dynamics. | Nguyen Thanh Binh, Nguyen Cong Thuong, Wei Luo, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | PAKDD | Intervention-Driven Predictive Framework for Modeling Healthcare Data. | Santu Rana, Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2014 | PERCOM | Fixed-lag particle filter for continuous context discovery using Indian Buffet Process. | Nguyen Cong Thuong, Sunil Gupta, Svetha Venkatesh, Dinh Q. Phung |
| 2014 | WISE | Effect of Mood, Social Connectivity and Age in Online Depression Community via Topic and Linguistic Analysis. | Bo Dao, Thin Nguyen, Dinh Q. Phung, Svetha Venkatesh |
| 2014 | WISE | Affective, Linguistic and Topic Patterns in Online Autism Communities. | Thin Nguyen, Thi V. Duong, Dinh Q. Phung, Svetha Venkatesh |
| 2014 | WISE | iPoll: Automatic Polling Using Online Search. | Thin Nguyen, Dinh Q. Phung, Wei Luo, Truyen Tran, Svetha Venkatesh |
| 2014 | SDM | Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions. | Sunil Kumar Gupta, Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | ACML | Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine. | Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | ADMA | EEG-Based User Authentication in Multilevel Security Systems. | Tien Pham, Wanli Ma, Dat Tran, Phuoc Nguyen, Dinh Q. Phung |
| 2013 | CHI | TOBY: early intervention in autism through technology. | Svetha Venkatesh, Dinh Q. Phung, Thi V. Duong, Stewart Greenhill, Brett Adams |
| 2013 | ICML | Factorial Multi-Task Learning : A Bayesian Nonparametric Approach. | Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | ICML | Thurstonian Boltzmann Machines: Learning from Multiple Inequalities. | Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | ICONIP | Topic Model Kernel: An Empirical Study towards Probabilistically Reduced Features for Classification. | Tien-Vu Nguyen, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | ICONIP | EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features. | Phuoc Nguyen, Dat Tran, Tan Vo, Xu Huang, Wanli Ma, Dinh Q. Phung |
| 2013 | ICONIP | A Study on the Feasibility of Using EEG Signals for Authentication Purpose. | Tien Pham, Wanli Ma, Dat Tran, Phuoc Nguyen, Dinh Q. Phung |
| 2013 | KDD | An integrated framework for suicide risk prediction. | Truyen Tran, Dinh Q. Phung, Wei Luo, Richard Harvey, Michael Berk, Svetha Venkatesh |
| 2013 | PAKDD | Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine. | Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | PAKDD | Split-Merge Augmented Gibbs Sampling for Hierarchical Dirichlet Processes. | Santu Rana, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | PAKDD | Clustering Patient Medical Records via Sparse Subspace Representation. | Budhaditya Saha, Duc-Son Pham, Dinh Q. Phung, Svetha Venkatesh |
| 2013 | PERCOM | Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes. | Nguyen Cong Thuong, Dinh Q. Phung, Sunil Gupta, Svetha Venkatesh |
| 2013 | SDM | Sparse Subspace Clustering via Group Sparse Coding. | Duc-Son Pham, Dinh Q. Phung, Budhaditya Saha, Svetha Venkatesh |
| 2012 | AAAI | A Sequential Decision Approach to Ordinal Preferences in Recommender Systems. | Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2012 | CVPR | Improved subspace clustering via exploitation of spatial constraints. | Duc-Son Pham, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh |
| 2012 | FUSION | Embedded Restricted Boltzmann Machines for fusion of mixed data types and applications in social measurements analysis. | Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2012 | ICDM | Sparse Subspace Representation for Spectral Document Clustering. | Budhaditya Saha, Dinh Q. Phung, Duc-Son Pham, Svetha Venkatesh |
| 2012 | ICPR | A nonparametric Bayesian Poisson gamma model for count data. | Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2012 | ICPR | Multi-modal abnormality detection in video with unknown data segmentation. | Tien-Vu Nguyen, Dinh Q. Phung, Santu Rana, Duc-Son Pham, Svetha Venkatesh |
| 2012 | ICWSM | A Sentiment-Aware Approach to Community Formation in Social Media. | Thin Nguyen, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2012 | UAI | A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning. | Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh |
| 2011 | CHI | A context-sensitive device to help people with autism cope with anxiety. | Marziya Mohammedali, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2011 | ICDM | Detection of Cross-Channel Anomalies from Multiple Data Channels. | Duc-Son Pham, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh |
| 2011 | ICWSM | Towards Discovery of Influence and Personality Traits through Social Link Prediction. | Thin Nguyen, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2011 | PAKDD | A Bayesian Framework for Learning Shared and Individual Subspaces from Multiple Data Sources. | Sunil Kumar Gupta, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2011 | PAKDD | Emotional Reactions to Real-World Events in Social Networks. | Thin Nguyen, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2011 | WISE | Prediction of Age, Sentiment, and Connectivity from Social Media Text. | Thin Nguyen, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2011 | SDM | Probabilistic Models over Ordered Partitions with Applications in Document Ranking and Collaborative Filtering. | Truyen Tran, Dinh Q. Phung, Svetha Venkatesh |
| 2010 | KDD | Nonnegative shared subspace learning and its application to social media retrieval. | Sunil Kumar Gupta, Dinh Q. Phung, Brett Adams, Truyen Tran, Svetha Venkatesh |
| 2010 | PAKDD | Classification and Pattern Discovery of Mood in Weblogs. | Thin Nguyen, Dinh Q. Phung, Brett Adams, Truyen Tran, Svetha Venkatesh |
| 2009 | PERCOM | High Accuracy Context Recovery using Clustering Mechanisms. | Dinh Q. Phung, Brett Adams, Kha Tran, Svetha Venkatesh, Mohan Kumar |
| 2009 | UAI | Ordinal Boltzmann Machines for Collaborative Filtering. | Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh |
| 2008 | AAAI | The Hidden Permutation Model and Location-Based Activity Recognition. | Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh, Hai Phan |
| 2008 | AusDM | Indoor Location Prediction Using Multiple Wireless Received Signal Strengths. | Kha Tran, Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2008 | PRICAI | Learning Discriminative Sequence Models from Partially Labelled Data for Activity Recognition. | Tran The Truyen, Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh |
| 2008 | PRICAI | Constrained Sequence Classification for Lexical Disambiguation. | Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh |
| 2008 | WWW | Computable social patterns from sparse sensor data. | Dinh Q. Phung, Brett Adams, Svetha Venkatesh |
| 2007 | AusDM | Preference Networks: Probabilistic Models for Recommendation Systems. | Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh |
| 2006 | CVPR | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition. | Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui |
| 2006 | ICPR | Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model. | Thi V. Duong, Dinh Q. Phung, Hung Hai Bui, Svetha Venkatesh |
| 2006 | ICPR | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment. | Dung T. Tran, Dinh Q. Phung |
| 2005 | CVPR | Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model. | Thi V. Duong, Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh |
| 2005 | CVPR | Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models. | Nam Thanh Nguyen, Dinh Q. Phung, Svetha Venkatesh, Hung Bui |
| 2004 | AAAI | Learning Hierarchical Hidden Markov Models with General State Hierarchy. | Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh |
| 2004 | ICIP | Automatically learning structural units in educational videos with the hierarchical hidden markov models. | Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui |
| 2004 | SSPR | Content Structure Discovery in Educational Videos Using Shared Structures in the Hierarchical Hidden Markov Models. | Dinh Q. Phung, Hung Hai Bui, Svetha Venkatesh |