ML Anthology

Le, Trung

76 publications

ICCV 2025 A Good Teacher Adapts Their Knowledge for Distillation Chengyao Qian, Trung Le, Mehrtash Harandi
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
CVPR 2025 Erasing Undesirable Influence in Diffusion Models Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi
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 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
ICLR 2025 Improved Training Technique for Latent Consistency Models Quan Dao, Khanh Doan, Di Liu, Trung Le, Dimitris N. Metaxas
ICML 2025 Improving Generalization with Flat Hilbert Bayesian Inference Tuan Truong, Quyen Tran, Ngoc-Quan Pham, Nhat Ho, Dinh Phung, Trung Le
ICLR 2025 NetFormer: An Interpretable Model for Recovering Dynamical Connectivity in Neuronal Population Dynamics Ziyu Lu, Wuwei Zhang, Trung Le, Hao Wang, Uygar Sümbül, Eric Todd SheaBrown, Lu Mi
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
ECML-PKDD 2025 PromptDSI: Prompt-Based Rehearsal-Free Continual Learning for Document Retrieval Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Yinwei Wei, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
ICML 2025 RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts Tuan Truong, Chau Nguyen, Huy Nguyen, Minh Le, Trung Le, Nhat Ho
ICLR 2025 Revisiting Prefix-Tuning: Statistical Benefits of Reparameterization Among Prompts Minh Le, Chau Nguyen, Huy Nguyen, Quyen Tran, Trung Le, Nhat Ho
NeurIPS 2025 SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding Trung Le, Hao Fang, Jingyuan Li, Tung Nguyen, Lu Mi, Amy L Orsborn, Uygar Sümbül, Eli Shlizerman
NeurIPS 2025 Token-Level Self-Play with Importance-Aware Guidance for Large Language Models Tue Le, Hoang Tran Vuong, Quyen Tran, Linh Ngo Van, Mehrtash Harandi, Trung Le
NeurIPS 2025 Unveiling M-Sharpness Through the Structure of Stochastic Gradient Noise Haocheng Luo, Mehrtash Harandi, Dinh Phung, Trung Le
ECCVW 2024 DiffAugment: Diffusion Based Long-Tailed Visual Relationship Recognition Parul Gupta, Tuan Nguyen, Abhinav Dhall, Munawar Hayat, Trung Le, Thanh-Toan Do
NeurIPS 2024 Enhancing Domain Adaptation Through Prompt Gradient Alignment Hoang Phan, Lam Tran, Quyen Tran, 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
NeurIPSW 2024 NetFormer: An Interpretable Model for Recovering Identity and Structure in Neural Population Dynamics Wuwei Zhang, Ziyu Lu, Trung Le, Hao Wang, Uygar Sümbül, Eric Todd SheaBrown, Lu Mi
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
ICML 2024 Sharpness-Aware Data Generation for Zero-Shot Quantization Hoang Anh Dung, Cuong Pham, Trung Le, Jianfei Cai, Thanh-Toan Do
CVPR 2024 Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung
NeurIPS 2023 AMAG: Additive, Multiplicative and Adaptive Graph Neural Network for Forecasting Neuron Activity Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy Orsborn, Eli Shlizerman
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 Learning Time-Invariant Representations for Individual Neurons from Population Dynamics Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül
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
ICLR 2022 A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, 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
MLJ 2022 Improving Kernel Online Learning with a Snapshot Memory Trung Le, Khanh Nguyen, Dinh Q. 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 STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers Trung Le, Eli Shlizerman
NeurIPS 2022 Stochastic Multiple Target Sampling Gradient Descent Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, 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
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
ICCV 2021 STEM: An Approach to Multi-Source Domain Adaptation with Guarantees Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung
IJCAI 2021 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
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
ICML 2020 Parameterized Rate-Distortion Stochastic Encoder Quan Hoang, Trung Le, Dinh Phung
IJCAI 2019 Learning Generative Adversarial Networks from Multiple Data Sources Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung
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
AAAI 2019 Robust Anomaly Detection in Videos Using Multilevel Representations Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung
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
IJCAI 2018 Geometric Enclosing Networks Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. 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
IJCAI 2017 Discriminative Bayesian Nonparametric Clustering Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui
NeurIPS 2017 Dual Discriminator Generative Adversarial Nets Tu Nguyen, Trung Le, Hung Vu, Dinh Phung
IJCAI 2017 Large-Scale Online Kernel Learning with Random Feature Reparameterization Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung
UAI 2017 Supervised Restricted Boltzmann Machines Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le
UAI 2016 Budgeted Semi-Supervised Support Vector Machine Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. 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
AISTATS 2016 Nonparametric Budgeted Stochastic Gradient Descent Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung