Phung, Dinh

78 publications

ICCV 2025 Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective Hoang Phan, Lam Tran, Quyen Tran, Ngoc Tran, Tuan Truong, Qi Lei, Nhat Ho, Dinh Phung, Trung Le

ICLR 2025 Boosting Multiple Views for Pretrained-Based Continual Learning Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Dinh Phung, Khoat Than, Trung Le

CVPR 2025 Enhancing Dataset Distillation via Non-Critical Region Refinement Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do, Dinh Phung

ICLR 2025 Fantastic Targets for Concept Erasure in Diffusion Models and Where to Find Them Anh Tuan Bui, Thuy-Trang Vu, Long Tung Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, Dinh Phung

NeurIPS 2025 GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, Shirui Pan

NeurIPS 2025 Geometry-Aware Collaborative Multi-Solutions Optimizer for Model Fine-Tuning with Parameter Efficiency Van-Anh Nguyen, Trung Le, Mehrtash Harandi, Ehsan Abbasnejad, Thanh-Toan Do, Dinh Phung

ICLRW 2025 Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts Anh Tuan Bui, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Phung

ICML 2025 Improving Generalization with Flat Hilbert Bayesian Inference Tuan Truong, Quyen Tran, Ngoc-Quan Pham, Nhat Ho, Dinh Phung, Trung Le

ICLR 2025 PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction Shangyu Chen, Zizheng Pan, Jianfei Cai, Dinh Phung

CVPR 2025 PanSplat: 4k Panorama Synthesis with Feed-Forward Gaussian Splatting Cheng Zhang, Haofei Xu, Qianyi Wu, Camilo Cruz Gambardella, Dinh Phung, Jianfei Cai

CVPR 2025 Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation Tung-Long Vuong, Hoang Phan, Vy Vo, Anh Bui, Thanh-Toan Do, Trung Le, Dinh Phung

ICML 2025 Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-Tuning Foundation Models Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Minh Nguyen, Dinh Phung, Trung Le

NeurIPS 2025 Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning Manh Luong, Khai Nguyen, Dinh Phung, Gholamreza Haffari, Lizhen Qu

NeurIPS 2025 Unveiling M-Sharpness Through the Structure of Stochastic Gradient Noise Haocheng Luo, Mehrtash Harandi, Dinh Phung, Trung Le

NeurIPS 2024 Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation Anh Bui, Long Vuong, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Phung

NeurIPS 2024 Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization Haocheng Luo, Tuan Truong, Tung Pham, Mehrtash Harandi, Dinh Phung, Trung Le

WACV 2024 Frequency Attention for Knowledge Distillation Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do

ECCV 2024 MetaAug: Meta-Data Augmentation for Post-Training Quantization Cuong Van Pham, Hoang Anh Dung, Cuong Cao Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do

CVPR 2024 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 Phung

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

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

ICLR 2024 Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation Manh Luong, Khai Nguyen, Nhat Ho, Gholamreza Haffari, Dinh Phung, Lizhen Qu

CVPR 2024 Taming Stable Diffusion for Text to 360 Panorama Image Generation Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, Jianfei Cai

CVPR 2024 Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung

WACV 2023 Adversarial Local Distribution Regularization for Knowledge Distillation Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Phung

ICLR 2023 An Additive Instance-Wise Approach to Multi-Class Model Interpretation Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Phung

NeurIPS 2023 Flat Seeking Bayesian Neural Networks Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, Trung Le

TMLR 2023 Generating Adversarial Examples with Task Oriented Multi-Objective Optimization Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Phung

AISTATS 2023 Global-Local Regularization via Distributional Robustness Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Phung

NeurIPS 2023 Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning Van Cuong Pham, Cuong Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do

NeurIPS 2023 Optimal Transport Model Distributional Robustness Van-Anh Nguyen, Trung Le, Anh Bui, Thanh-Toan Do, Dinh Phung

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

AISTATS 2022 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 Tran, Dinh Phung

AISTATS 2022 Particle-Based Adversarial Local Distribution Regularization Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Phung

AISTATS 2022 Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen

ICLR 2022 A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Phung

CVPR 2022 Bridging Global Context Interactions for High-Fidelity Image Completion Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai, Dinh Phung

UAI 2022 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 Phung

NeurIPS 2022 MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation Chuanxia Zheng, Tung-Long Vuong, Jianfei Cai, Dinh Phung

ICML 2022 On Transportation of Mini-Batches: A Hierarchical Approach Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho

ICLRW 2022 ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Phung

NeurIPS 2022 Stochastic Multiple Target Sampling Gradient Descent Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung

NeurIPS 2021 Exploiting Domain-Specific Features to Enhance Domain Generalization Manh-Ha Bui, Toan Tran, Anh Tran, Dinh Phung

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

ICML 2021 LAMDA: Label Matching Deep Domain Adaptation Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung

UAI 2021 Most: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Phung

ICLR 2021 Neural Topic Model via Optimal Transport He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

JMLR 2021 On Efficient Multilevel Clustering via Wasserstein Distances Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung

NeurIPS 2021 On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources Trung Phung, Trung Le, Tung-Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung

ACML 2021 Quaternion Graph Neural Networks Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

ICCV 2021 STEM: An Approach to Multi-Source Domain Adaptation with Guarantees Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung

ECML-PKDD 2020 A Self-Attention Network Based Node Embedding Model Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

ECCV 2020 Improving Adversarial Robustness by Enforcing Local and Global Compactness Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung

NeurIPS 2020 OTLDA: A Geometry-Aware Optimal Transport Approach for Topic Modeling Viet Huynh, He Zhao, Dinh Phung

ICML 2020 Parameterized Rate-Distortion Stochastic Encoder Quan Hoang, Trung Le, Dinh Phung

AISTATS 2020 Variational Autoencoders for Sparse and Overdispersed Discrete Data He Zhao, Piyush Rai, Lan Du, Wray Buntine, Dinh Phung, Mingyuan Zhou

ICLR 2019 Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu

AISTATS 2019 Probabilistic Multilevel Clustering via Composite Transportation Distance Nhat Ho, Viet Huynh, Dinh Phung, Michael Jordan

IJCAI 2019 Three-Player Wasserstein GAN via Amortised Duality Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung

ACML 2018 Batch Normalized Deep Boltzmann Machines Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Phung

ACML 2018 Clustering Induced Kernel Learning Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Phung

ICLR 2018 MGAN: Training Generative Adversarial Nets with Multiple Generators Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung

JMLR 2017 Approximation Vector Machines for Large-Scale Online Learning Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

NeurIPS 2017 Dual Discriminator Generative Adversarial Nets Tu Nguyen, Trung Le, Hung Vu, Dinh Phung

ICML 2017 Multilevel Clustering via Wasserstein Means Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung

NeurIPS 2016 Dual Space Gradient Descent for Online Learning Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung

ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen, Trung Le, Vu Nguyen, Tu Nguyen, Dinh Phung

MLHC 2016 Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh

ACML 2015 Streaming Variational Inference for Dirichlet Process Mixtures Viet Huynh, Dinh Phung, Svetha Venkatesh

ICML 2014 Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts Tien Vu Nguyen, Dinh Phung, Xuanlong Nguyen, Swetha Venkatesh, Hung Bui

ACML 2014 Preface Dinh Phung, Hang Li

ICML 2013 Factorial Multi-Task Learning : A Bayesian Nonparametric Approach Sunil Gupta, Dinh Phung, Svetha Venkatesh

ACML 2013 Learning Parts-Based Representations with Nonnegative Restricted Boltzmann Machine Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh

ICML 2013 Thurstonian Boltzmann Machines: Learning from Multiple Inequalities Truyen Tran, Dinh Phung, Svetha Venkatesh

ACML 2012 Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis Truyen Tran, Dinh Phung, Svetha Venkatesh

ACML 2012 Learning from Ordered Sets and Applications in Collaborative Ranking Truyen Tran, Dinh Phung, Svetha Venkatesh

ACML 2011 Mixed-Variate Restricted Boltzmann Machines Truyen Tran, Dinh Phung, Svetha Venkatesh

NeurIPS 2008 Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data Tran T. Truyen, Dinh Phung, Hung Bui, Svetha Venkatesh