L o a d i n g

News

  • Our three papers have been accepted by Neurips 2025. Congratulations!
    1. “ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge”
    2. “Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data”
    3.”Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation”
  • Our three papers have been accepted by ICCV 2025. Congratulations!
    1. Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
    2. Region-Level Data Attribution for Text-to-Image Generative Models 
    3. ConstStyle: Robust Domain Generalization with Unified Style Transformation
  • Our paper “Unleashing SAM for Few-Shot Medical Image Segmentation with Dual-Encoder and Automated Prompting” has been accepted by MICCAI 2025. Congratulations!
  • Our paper “Localizing Before Answering: A Benchmark for Grounded Medical Visual Question Answering” has been accepted by IJCAI 2025. Congratulations!
  • Our paper “SAFA: Handling Sparse and Scarce Data in Federated Learning with Accumulative Learning” has been accepted by IEEE Transactions on Computers. Congratulations!
  • Our paper “NeurFlow: Interpreting Neural Networks through Critical Neuron Groups and Functional Interactions” has been accepted by ICLR 2025. Congratulations!
  • Our paper “Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models” has been accepted by ISBI 2025. Congratulations!
  • Our paper “Foundation Model and Temporal Priors-guided Transductive Few-shot Action Recognition” has been accepted by ICASSP 2025. Congratulations!
  • Two papers: “CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach” and “Sign Language Recognition: A Large-scale Multi-view Dataset and Comprehensive Evaluation” have been accepted by WACV 2025. Congratulations!
  • Our paper “Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques” has been accepted by NeurIPS 2024. Congratulations!
  • Our paper “CARER – ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction” has been accepted by EMNLP 2024. Congratulations!
  • Our paper “Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience” has been accepted by ECML PKDD 2024. Congratulations!
  • Our paper “Traffic Engineering in Large-scale Networks via Multi-Agent Deep Reinforcement Learning with Joint-Training” has been accepted by ICCCN 2024. Congratulations!
  • Our paper “Boosting Offline Optimizers with Surrogate Sensitivity”, has been accepted by ICML 2024. Congratulations!
  • Our paper “Enhancing the Generalization of Personalized Federated Learning with Multi-head Model and Ensemble Voting” has been accepted by IPDPS 2024. Congratulations!

Selected Publications

2025

  1. Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, “ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge”, The Thirty-ninth Annual Conference on Neural Information Processing Systems (Neurips 2025).
  2. Manh Duong Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Phi Le Nguyen, “Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data”, The Thirty-ninth Annual Conference on Neural Information Processing Systems (Neurips 2025).
  3. Huu Tien Nguyen, Dac Thai Nguyen, Duc Nguyen The Minh, Trung Thanh Nguyen, Thao Nguyen Truong, Hieu Pham, Johan Barthelemy, Tran Minh Quan, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, Mai Hong Son, Chau Quynh Anh, Thanh Trung Nguyen, Phi Le Nguyen, “Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation”, The Thirty-ninth Annual Conference on Neural Information Processing Systems (Neurips 2025)
  4. Thanh Trung Nguyen, Minh Hai Vu, Kien Nguyen, Phi Le Nguyen, Sekiya, “Evaluating MPQUIC Schedulers in Dynamic Wireless Networks with 2D and 3D Mobility”, Computer Networks Journal 2025.
  5. Yunfei Wang, Yeqin Zhang, Yuyang Wu, Liang Lu, Phi Le Nguyen, Xiaoliang Wang, Nguyen Cam-Tu, “MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms?”, The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
  6. Trong Bang Nguyen, Phi Le Nguyen, Simon Lucey, Minh Hoai, “Region-Level Data Attribution for Text-to-Image Generative Models”, International Conference on Computer Vision 2025 (ICCV 2025).
  7. Thu Hang Phung, Manh Duong Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Trong Nghia Hoang, Phi Le Nguyen, “Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data”, International Conference on Computer Vision 2025 (ICCV 2025).
  8. Ta Duc Huy, Duy Anh Huynh, Yutong Xie, Yuankai Qi, Qi Chen, Phi Le Nguyen, Sen Kim Tran, Son Lam Phung, Anton van den Hengel, Zhibin Liao, Minh-Son To, Johan W. Verjans, Vu Minh Hieu Phan, “Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding”, International Conference on Computer Vision 2025 (ICCV 2025).
  9. Nam Duong Tran, Nam Nguyen Phuong, Hieu Pham, Phi Le Nguyen, My T. Thai, “ConstStyle: Robust Domain Generalization with Unified Style Transformation”, International Conference on Computer Vision 2025 (ICCV 2025).
  10. Thanh Tam Nguyen, Phi Le Nguyen, Nguyen Quoc Viet Hung et al., “A Survey of Machine Unlearning”, ACM Transactions on Intelligent Systems and Technology 2025.
  11. Ta Duc Huy, Phi Le Nguyen, Minh Hieu Phan, et al., “Too Tall for the Crib: When Vision-Language Models Outgrow Pediatric X-rays”, Medical Image Computing and Computer Assisted Intervention 2025 (MICCAI 2025).
  12. Cuong Pham, Phi Le Nguyen, Thanh Trung Nguyen, Minh Hieu Phan, Binh Nguyen, “Unleashing SAM for Few-Shot Medical Image Segmentation with Dual-Encoder and Automated Prompting”, Medical Image Computing and Computer Assisted Intervention 2025 (MICCAI 2025).
  13. Manh Cuong Dao, Phi Le Nguyen, Huy Hieu Pham, Thanh Hung Nguyen, Peng Chen, Mohamed Wahib and Thao Nguyen Truong, “Noisy Data-based Attack: a New Type of Untargeted Attack in Federated Learning and Its Countermeasures”, Future Generation Computer Systems, 2025.
  14. Nguyen Nang Hung, Truong Thao Nguyen, Trong Nghia Hoang, Hieu H. Pham, Thanh Hung Nguyen, and Nguyen Phi Le, “SAFA: Handling Sparse and Scarce Data in Federated Learning with Accumulative Learning”, IEEE Transactions on Computers (accepted).
  15. Tue Minh Cao, Nhat Hoang-Xuan, Hieu Pham, Phi Le Nguyen, My T. Thai, “NeurFlow: Interpreting Neural Networks through Critical Neuron Groups and Functional Interactions”, The Thirteenth International Conference on Learning Representations, ICLR 2025 (accepted).
  16. Manh-Duong Nguyen, Dac Thai Nguyen, Viet Trung Nguyen, Homi Yamada, Huy Hieu Pham, Phi Le Nguyen, “Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models”, 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI).
  17. Bach Vu, Hoang Nguyen, Quang Nguyen, Duong Le, Hieu Pham, Phi Le Nguyen, Lam Nguyen, “Foundation Model and Temporal Priors-guided Transductive Few-shot Action Recognition”, 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2025 Main Tracks).
  18. Thai Nguyen, Trung Thanh NGUYEN, Tien Nguyen, Trung Nguyen Thanh, Hieu Pham, Thanh Hung Nguyen, Truong Thao Nguyen, Phi Le Nguyen, “CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach”, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025).
  19. Son Dinh, Dung Nguyen, Duc-Tri Tran, Dang-Huy Pham-Nguyen, Thuan Hieu Tran, Tong Anh, Quang Huy Hoang, Phi Le Nguyen, “Sign Language Recognition: A Large-scale Multi-view Dataset and Comprehensive Evaluation”, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025).

2024

  1. Le Anh Duc Vu, Minh Hai Vu, Bao Ngoc Tran, Minh Tung Hoang, Duc Anh Nguyen, Minh Quan Hoang, Phi Le Nguyen, “An Automated PM2.5 Analysis and Prediction System with Encoder-Decoder Architecture and Continual Learning Mechanism”, the 22nd IEEE International Conference on Embedded and Ubiquitous Computing (EUC 2024) (Best paper award).
  2. Duong Nguyen, Phi Le Nguyen, Truong Nguyen, Hieu Pham, and Duc Tran, “FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks”, IEEE BigData 2024.
  3. Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen and Phi Le Nguyen, “FedCert: Federated Accuracy Certification”, The 22nd International Symposium on Network Computing and Applications (NCA 2024).
  4. Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen and Thanh Trung Huynh, “FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization”, The 22nd International Symposium on Network Computing and Applications (NCA 2024).
  5. Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, “Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques”,The Thirty-eighth Annual Conference on Neural Information Processing Systems, NeurIPS 2024.
  6. Tuan Dung Nguyen, Thanh Trung Huynh, Minh Hieu Phan, Quoc Viet Hung Nguyen, Phi Le Nguyen, “CARER – ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction”, The 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP.
  7. Dong Duc Anh Nguyen, Minh Hieu Nguyen, Phi Le Nguyen, Jun Jo, Hongzhi Yin, Thanh Tam Nguyen, “Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNs”, The 20th International Conference on Advanced Data Mining and Applications 2024.
  8. Phi Le Nguyen et al., “Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder”, ICDM 2024.
  9. Minh Hai Vu, Thanh Trung Nguyen, Thi Ha Ly Dinh, Phi Le Nguyen, Kien Nguyen, “LoGra: an LSTM-DDPG Integrated MPQUIC Scheduler for Mobile Video Streaming”, VTC 2024.
  10. Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Thanh Tam Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer, “Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024).
  11. Van An Le, Duc Long Nguyen, Phi Le Nguyen, Yusheng Ji, “Traffic Engineering in Large-scale Networks via Multi-Agent Deep Reinforcement Learning with Joint-Training”, The 33rd International Conference on Computer Communications and Networks (ICCCN 2024).
  12. Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, “Boosting Offline Optimizers with Surrogate Sensitivity”, The 41st International Conference on Machine Learning (ICML 2024).
  13. Tuan Dung Nguyen, Ngoc Anh Tong, Viet Hung Nguyen, Phu Binh Nguyen, Phi Le Nguyen, Trung Huynh, “Hierarchical Federated Learning in MEC Networks with Knowledge Distillation”, the International Joint Conference on Neural Networks (IJCNN 2024).
  14. Thu Hang Phung, Thanh Hung Nguyen, Viet Hung Nguyen, Phu Binh Nguyen, Phi Le Nguyen, Trung Huynh, “A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning”, the International Joint Conference on Neural Networks (IJCNN 2024).
  15. Duc Long Nguyen, Thao Nguyen Truong, Phi Le Nguyen, “Combating Quality Distortion in Federated Learning with Collaborative Data Selection”, 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024).
  16. Van An Le, Nam Duong Tran, Phuong Nam Nguyen, Thanh Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Yusheng Ji, “Enhancing the Generalization of Personalized Federated Learning with Multi-head Model and Ensemble Voting”, 38th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2024).
  17. Thanh Trung Nguyen, Minh Hai Vu, Thi Ha Ly Dinh, Phi Le Nguyen, Kien Nguyen, “MuLeS: a Multi-client Learning-based MPQUIC Scheduler”, IEEE Consumer Communications & Networking Conference (CCNC 2024).
  18. Quang Ha Pham, Nguyen Nang Hung, Hieu Pham, Nguyen Thanh-Hung, Truong Thao Nguyen, Phi Le Nguyen, “SEM: A Simple Yet Efficient Model-agnostic Local Training Mechanism to Tackle Data Sparsity and Scarcity in Federated Learning”, The Eleventh International Symposium on Computing and Networking (CANDAR 2024) (Best paper reward).
  19. Thanh Trung Huynh, Trong Bang Nguyen, Thanh Toan Nguyen, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, “Certified Unlearning for Federated Recommendation,” ACM Transactions on Information Systems, 2024.
  20. Thanh Trung Nguyen, Minh Hai Vu, Thi Ha Ly Dinh, Thanh Hung Nguyen, Phi Le Nguyen, Kien Nguyen, “FQ-SAT: a Fuzzy Q-learning-based MPQUIC Scheduler for Data Transmission Optimization,” Computer Communications Journal, 2024.
  21. Toan Nguyen, Minh Hieu Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Hien Pham, Thanh Tam Nguyen, “On-Device Diagnostic Recommendation with Heterogeneous Federated BlockNets”, Science China Information Sciences, 2024.
  22. Thanh Tam Nguyen, Thanh Trung Huynh, Toan Nguyen, Phi Le Nguyen, Hongzhi Yin, Thanh Tam Nguyen, Quoc Viet Hung Nguyen, “Privacy-Preserving Explainable AI: A Survey”, Science China Information Sciences, 2024.
  23. Darnbi Sakonga, Viet Hung Vu, Thanh Trung Huynh, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, “Higher-order Knowledge-enhanced Recommendation with Heterogeneous Hypergraph Multi-Attention”, Information Sciences, 2024.
  24. Trung Thanh Nguyen, Phi Le Nguyen, Yasutomo Kawanishi, Takahiro Komanishi, Takahiro, Ichiro Ide, “Zero-shot Pill-Prescription Matching with Graph Convolutional Network and Contrastive Learning”, IEEE Access, 2024.
  25. Pham Minh Khiem, Phi Le Nguyen, Vu Viet Hung, Truong Thao Nguyen, Vo-Van Hoa, and Thanh Ngo-Duc, “A Data-driven Approach for High Accurate Spatiotemporal Precipitation Estimation”, Neural Computing and Applications Journal, 2024.
  26. Viet Hung Vu, Duc Long Nguyen, Thanh Hung Nguyen, Nguyen Quoc Viet Hung, Phi Le Nguyen, and Huynh Thanh Trung, “Self-supervised air quality estimation with graph neural network assistance and attention enhancement,” Neural Computing and Applications Journal, 2024.
  27. Phi Le Nguyen et al., “GAMMA: a Universal Model for Calibrating Sensory Data of Multiple Low-Cost Air Monitoring Devices”, Engineering Applications of Artificial Intelligence, 2024.