A new support vector machine method for medical image classification. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (EUVIP, 2010)

A Theoretical Framework for Multi-sphere Support Vector Data Description. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (ICONIP (2), 2010)

An optimal sphere and two large margins approach for novelty detection. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (IJCNN, 2010)

Fuzzy support vector machines for age and gender classification. - Phuoc Nguyen; Trung Le 001; Dat Tran 001; Xu Huang 001; Dharmendra Sharma 001 (INTERSPEECH, 2010)

Enhancing Performance of SVM-Based Brain-Computer Interface Systems. - Tuan Hoang; Phuoc Nguyen; Trung Le 001; Dat Tran 001; Dharmendra Sharma 001 (Aust. J. Intell. Inf. Process. Syst., 2010)

Generalised Support Vector Machine for Brain-Computer Interface. - Trung Le 001; Dat Tran 001; Tuan Hoang; Wanli Ma; Dharmendra Sharma 001 (ICONIP (1), 2011)

A Novel Parameter Refinement Approach to One Class Support Vector Machine. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (ICONIP (2), 2011)

Multi-Sphere Support Vector Clustering. - Trung Le 001; Dat Tran 001; Phuoc Nguyen; Wanli Ma; Dharmendra Sharma 001 (ICONIP (2), 2011)

Multiple distribution data description learning method for novelty detection. - Trung Le 001; Dat Tran 001; Phuoc Nguyen; Wanli Ma; Dharmendra Sharma 001 (IJCNN, 2011)

Multiple Distribution Data Description Learning Algorithm for Novelty Detection. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (PAKDD (2), 2011)

Fuzzy Multi-sphere Support Vector Data Description. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (FUZZ-IEEE, 2012)

Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. - Tuan Hoang; Dat Tran 001; Khoa Truong; Trung Le 001; Xu Huang 001; Dharmendra Sharma 001; Toi Vo (ICONIP (2), 2012)

Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. - Trung Le 001; Dat Tran 001; Tuan Hoang; Dharmendra Sharma 001 (ICONIP (3), 2012)

Deterministic Annealing Multi-Sphere Support Vector Data Description. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (ICONIP (3), 2012)

A unified model for support vector machine and support vector data description. - Trung Le 001; Dat Tran 001; Wanli Ma; Dharmendra Sharma 001 (IJCNN, 2012)

Maximal margin learning vector quantisation. - Trung Le 001; Dat Tran 001; Van Nguyen 002; Wanli Ma (IJCNN, 2013)

Fuzzy entropy semi-supervised support vector data description. - Trung Le 001; Dat Tran 001; Tien Tran; Khanh Nguyen; Wanli Ma (IJCNN, 2013)

Fuzzy Multi-Sphere Support Vector Data Description. - Trung Le 001; Dat Tran 001; Wanli Ma (PAKDD (2), 2013)

EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. - Phuoc Nguyen; Dat Tran 001; Trung Le 001; Xu Huang 001; Wanli Ma (PAKDD (1), 2013)

Proximity multi-sphere support vector clustering. - Trung Le 001; Dat Tran 001; Phuoc Nguyen; Wanli Ma; Dharmendra Sharma 001 (Neural Comput. Appl., 2013)

Robust Support Vector Machine. - Trung Le 001; Dat Tran 001; Wanli Ma; Thien Pham; Phuong Duong; Minh Nguyen 004 (IJCNN, 2014)

Using EEG artifacts for BCI applications. - Wanli Ma; Dat Tran 001; Trung Le 001; Hong Lin; Shang-Ming Zhou (IJCNN, 2014)

Kernel-based semi-supervised learning for novelty detection. - Van Nguyen 002; Trung Le 001; Thien Pham; Mi Dinh; Thai Hoang Le (IJCNN, 2014)

Graph-based semi-supervised Support Vector Data Description for novelty detection. - Phuong Duong; Van Nguyen 002; Mi Dinh; Trung Le 001; Dat Tran 001; Wanli Ma (IJCNN, 2015)

Least square Support Vector Machine for large-scale dataset. - Khanh Nguyen; Trung Le 001; Vinh Lai; Duy Nguyen; Dat Tran 001; Wanli Ma (IJCNN, 2015)

Fast One-Class Support Vector Machine for Novelty Detection. - Trung Le 001; Dinh Q. Phung; Khanh Nguyen; Svetha Venkatesh (PAKDD (2), 2015)

Multiple Kernel Learning with Data Augmentation. - Khanh Nguyen; Trung Le 001; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (ACML, 2016)

Nonparametric Budgeted Stochastic Gradient Descent. - Trung Le 001; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (AISTATS, 2016)

Fast Support Vector Clustering. - Tung Pham 001; Trung Le 001; Thai Hoang Le; Dat Tran 001 (ESANN, 2016)

One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems. - Vu Nguyen 001; Tu Dinh Nguyen; Trung Le 001; Svetha Venkatesh; Dinh Q. Phung (ICDM, 2016)

Distributed data augmented support vector machine on Spark. - Tu Dinh Nguyen; Vu Nguyen 001; Trung Le 001; Dinh Q. Phung (ICPR, 2016)

Fast Kernel-based method for anomaly detection. - Anh Le; Trung Le 001; Khanh Nguyen; Van Nguyen 002; Thai Hoang Le; Dat Tran 001 (IJCNN, 2016)

Fuzzy Kernel Stochastic Gradient Descent machines. - Tuan Nguyen 004; Phuong Duong; Trung Le 001; Anh Le; Viet Ngo; Dat Tran 001; Wanli Ma (IJCNN, 2016)

Dual Space Gradient Descent for Online Learning. - Trung Le 001; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (NIPS, 2016)

Sparse Adaptive Multi-hyperplane Machine. - Khanh Nguyen; Trung Le 001; Vu Nguyen 001; Dinh Q. Phung (PAKDD (1), 2016)

Budgeted Semi-supervised Support Vector Machine . - Trung Le 001; Phuong Duong; Mi Dinh; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (UAI, 2016)

Scalable Support Vector Machine for Semi-supervised Learning. - Trung Le 001; Khanh Nguyen; Van Nguyen 002; Vu Nguyen 001; Dinh Q. Phung (CoRR, 2016)

Mixture of hyperspheres for novelty detection. - Duy Nguyen; Vinh Lai; Khanh Nguyen; Trung Le 001 (Vietnam. J. Comput. Sci., 2016)

GoGP: Fast Online Regression with Gaussian Processes. - Trung Le 001; Khanh Nguyen; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (ICDM, 2017)

Large-scale Online Kernel Learning with Random Feature Reparameterization. - Tu Dinh Nguyen; Trung Le 001; Hung Bui 001; Dinh Q. Phung (IJCAI, 2017)

Discriminative Bayesian Nonparametric Clustering. - Vu Nguyen 001; Dinh Q. Phung; Trung Le 001; Hung Bui 001 (IJCAI, 2017)

Dual Discriminator Generative Adversarial Nets. - Tu Dinh Nguyen; Trung Le 001; Hung Vu; Dinh Q. Phung (NIPS, 2017)

Supervised Restricted Boltzmann Machines. - Tu Dinh Nguyen; Dinh Q. Phung; Viet Huynh; Trung Le 001 (UAI, 2017)

Multi-Generator Generative Adversarial Nets. - Quan Hoang; Tu Dinh Nguyen; Trung Le 001; Dinh Q. Phung (CoRR, 2017)

Analogical-based Bayesian Optimization. - Trung Le 001; Khanh Nguyen; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2017)

Scalable Support Vector Clustering Using Budget. - Tung Pham 001; Trung Le 001; Hang Dang (CoRR, 2017)

KGAN: How to Break The Minimax Game in GAN. - Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2017)

Approximation Vector Machines for Large-scale Online Learning. - Trung Le 001; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (J. Mach. Learn. Res., 2017)

Fast support vector clustering. - Tung Pham 001; Hang Dang; Trung Le 001; Thai Hoang Le (Vietnam. J. Comput. Sci., 2017)

Clustering Induced Kernel Learning. - Khanh Nguyen; Nhan Dam; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (ACML, 2018)

Batch Normalized Deep Boltzmann Machines. - Hung Vu; Tu Dinh Nguyen; Trung Le 001; Wei Luo 001; Dinh Q. Phung (ACML, 2018)

MGAN: Training Generative Adversarial Nets with Multiple Generators. - Quan Hoang; Tu Dinh Nguyen; Trung Le 001; Dinh Q. Phung (ICLR (Poster), 2018)

Bayesian Multi-Hyperplane Machine for Pattern Recognition. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (ICPR, 2018)

Geometric Enclosing Networks. - Trung Le 001; Hung Vu; Tu Dinh Nguyen; Dinh Q. Phung (IJCAI, 2018)

Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung; Geoffrey I. Webb (KDD, 2018)

Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. - Hung Nguyen; Van Nguyen 002; Thin Nguyen; Mark E. Larsen; Bridianne O'Dea; Duc Thanh Nguyen; Trung Le 001; Dinh Q. Phung; Svetha Venkatesh; Helen Christensen (WISE (2), 2018)

Theoretical Perspective of Deep Domain Adaptation. - Trung Le 001; Khanh Nguyen; Dinh Q. Phung (CoRR, 2018)

Robust Anomaly Detection in Videos Using Multilevel Representations. - Hung Vu; Tu Dinh Nguyen; Trung Le 001; Wei Luo 001; Dinh Q. Phung (AAAI, 2019)

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. - Tue Le; Tuan Nguyen 004; Trung Le 001; Dinh Q. Phung; Paul Montague; Olivier Y. de Vel; Lizhen Qu (ICLR (Poster), 2019)

Three-Player Wasserstein GAN via Amortised Duality. - Nhan Dam; Quan Hoang; Trung Le 001; Tu Dinh Nguyen; Hung Bui 001; Dinh Phung 001 (IJCAI, 2019)

Learning Generative Adversarial Networks from Multiple Data Sources. - Trung Le 001; Quan Hoang; Hung Vu; Tu Dinh Nguyen; Hung Bui 001; Dinh Q. Phung (IJCAI, 2019)

Deep Domain Adaptation for Vulnerable Code Function Identification. - Van Nguyen 002; Trung Le 001; Tue Le; Khanh Nguyen; Olivier Y. DeVel; Paul Montague; Lizhen Qu; Dinh Q. Phung (IJCNN, 2019)

When Can Neural Networks Learn Connected Decision Regions? - Trung Le 001; Dinh Q. Phung (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)

GoGP: scalable geometric-based Gaussian process for online regression. - Trung Le 001; Khanh Nguyen; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (Knowl. Inf. Syst., 2019)

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. - Quan Hung Tran; Nhan Dam; Tuan Manh Lai; Franck Dernoncourt; Trung Le 001; Nham Le; Dinh Phung 001 (COLING, 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)

Parameterized Rate-Distortion Stochastic Encoder. - Quan Hoang; Trung Le 001; Dinh Phung 001 (ICML, 2020)

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

Stein Variational Gradient Descent with Variance Reduction. - Nhan Dam; Trung Le 001; Viet Huynh; Dinh Phung 001 (IJCNN, 2020)

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. - Mahmoud Hossam; Trung Le 001; Viet Huynh; Michael Papasimeon; Dinh Phung 001 (IJCNN, 2020)

Code Pointer Network for Binary Function Scope Identification. - Van Nguyen 002; Trung Le 001; Tue Le; Khanh Nguyen; Olivier Y. de Vel; Paul Montague; Dinh Phung 001 (IJCNN, 2020)

Code Action Network for Binary Function Scope Identification. - Van Nguyen 002; Trung Le 001; Tue Le; Khanh Nguyen; Olivier Y. de Vel; Paul Montague; John C. Grundy; Dinh Phung 001 (PAKDD (1), 2020)

Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection. - Tuan Nguyen 004; Trung Le 001; Khanh Nguyen; Olivier Y. de Vel; Paul Montague; John C. Grundy; Dinh Phung 001 (PAKDD (2), 2020)

Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection. - Van Nguyen 002; Trung Le 001; Olivier Y. de Vel; Paul Montague; John C. Grundy; Dinh Phung 001 (PAKDD (1), 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)

STEM: An approach to Multi-source Domain Adaptation with Guarantees. - Van-Anh Nguyen; Tuan Nguyen 004; Trung Le 001; Quan Hung Tran; Dinh Phung 001 (ICCV, 2021)

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

LAMDA: Label Matching Deep Domain Adaptation. - Trung Le 001; Tuan Nguyen 004; Nhat Ho; Hung Bui 001; Dinh Phung 001 (ICML, 2021)

TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport. - Tuan Nguyen 004; Trung Le 001; Nhan Dam; Quan Hung Tran; Truyen Nguyen; Dinh Q. Phung (IJCAI, 2021)

Information-theoretic Source Code Vulnerability Highlighting. - Van Nguyen 002; Trung Le 001; Olivier Y. de Vel; Paul Montague; John Grundy 001; Dinh Phung 001 (IJCNN, 2021)

On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. - Trung Phung; Trung Le 001; Long Vuong; Toan Tran 003; Anh Tran 001; Hung Bui 001; Dinh Q. Phung (NeurIPS, 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)

BoMb-OT: On Batch of Mini-batches Optimal Transport. - Khai Nguyen; Quoc Nguyen; Nhat Ho; Tung Pham 001; Hung Bui 001; Dinh Phung 001; Trung Le 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)

Text Generation with Deep Variational GAN. - Mahmoud Hossam; Trung Le 001; Michael Papasimeon; Viet Huynh; Dinh Phung 001 (CoRR, 2021)

On Label Shift in Domain Adaptation via Wasserstein Distance. - Trung Le 001; Dat Do; Tuan Nguyen 004; Huy Nguyen; Hung Bui 001; Nhat Ho; Dinh Q. Phung (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)

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)

On Transportation of Mini-batches: A Hierarchical Approach. - Khai Nguyen; Dang Nguyen 002; Quoc Dinh Nguyen; Tung Pham 001; Hung Bui 001; Dinh Phung 001; Trung Le 001; Nhat Ho (ICML, 2022)

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. - Van-Anh Nguyen; Dai Quoc Nguyen; Van Nguyen 002; Trung Le 001; Quan Hung Tran; Dinh Phung 001 (ICSE-Companion, 2022)

Stochastic Multiple Target Sampling Gradient Descent. - Hoang Phan; Ngoc Tran; Trung Le 001; Toan Tran 003; Nhat Ho; Dinh Phung 001 (NeurIPS, 2022)

VulRepair: a T5-based automated software vulnerability repair. - Michael Fu; Chakkrit Tantithamthavorn; Trung Le 001; Van Nguyen 002; Dinh Q. Phung (ESEC/SIGSOFT FSE, 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)

Global-Local Regularization Via Distributional Robustness. - Hoang Phan; Trung Le 001; Trung Phung; Tuan-Anh Bui; Nhat Ho; Dinh Q. Phung (CoRR, 2022)

Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle. - Van Nguyen 002; Trung Le 001; Chakkrit Tantithamthavorn; John C. Grundy; Hung Nguyen; Dinh Q. Phung (CoRR, 2022)

An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability. - Van Nguyen 002; Trung Le 001; Chakkrit Tantithamthavorn; John C. Grundy; Hung Nguyen; Seyit Camtepe; Paul Quirk; 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)

Vision Transformer Visualization: What Neurons Tell and How Neurons Behave? - Van-Anh Nguyen; Khanh Pham Dinh; Long Tung Vuong; Thanh-Toan Do; Quan Hung Tran; Dinh Q. Phung; Trung Le 001 (CoRR, 2022)

Improving Multi-task Learning via Seeking Task-based Flat Regions. - Hoang Phan; Lam Tran; Ngoc N. Tran; Nhat Ho; Dinh Q. Phung; Trung Le 001 (CoRR, 2022)

Continual Learning with Optimal Transport based Mixture Model. - Quyen Tran; Hoang Phan; Khoat Than; Dinh Q. Phung; Trung Le 001 (CoRR, 2022)

Multiple Perturbation Attack: Attack Pixelwise Under Different $ ell_p$-norms For Better Adversarial Performance. - Ngoc N. Tran; Anh Tuan Bui; Dinh Q. Phung; Trung Le 001 (CoRR, 2022)

Improving kernel online learning with a snapshot memory. - Trung Le 001; Khanh Nguyen; Dinh Q. Phung (Mach. Learn., 2022)

Robust Variational Learning for Multiclass Kernel Models With Stein Refinement. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Geoffrey I. Webb; Dinh Phung 001 (IEEE Trans. Knowl. Data Eng., 2022)

Global-Local Regularization Via Distributional Robustness. - Hoang Phan; Trung Le 001; Trung Phung; Anh Tuan Bui; Nhat Ho; Dinh Q. Phung (AISTATS, 2023)

ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? - Michael Fu; Chakkrit Kla Tantithamthavorn; Van Nguyen 002; Trung Le 001 (APSEC, 2023)

An Additive Instance-Wise Approach to Multi-class Model Interpretation. - Vy Vo; Van Nguyen 002; Trung Le 001; Quan Hung Tran; Gholamreza Haffari; Seyit Camtepe; Dinh Phung 001 (ICLR, 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)

Optimal Transport Model Distributional Robustness. - Van-Anh Nguyen; Trung Le 001; Anh Tuan Bui; Thanh-Toan Do; Dinh Q. Phung (NeurIPS, 2023)

Flat Seeking Bayesian Neural Networks. - Van-Anh Nguyen; Tung-Long Vuong; Hoang Phan; Thanh-Toan Do; Dinh Q. Phung; Trung Le 001 (NeurIPS, 2023)

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. - Van Cuong Pham; Cuong C. Nguyen; Trung Le 001; Dinh Phung 001; Gustavo Carneiro 001; Thanh-Toan Do (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)

Flat Seeking Bayesian Neural Networks. - Van-Anh Nguyen; Long Tung Vuong; Hoang Phan; Thanh-Toan Do; Dinh Q. Phung; Trung Le 001 (CoRR, 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)

Hyperbolic Geometry in Computer Vision: A Survey. - Pengfei Fang; Mehrtash Harandi; Trung Le 001; Dinh Q. Phung (CoRR, 2023)

Sharpness & Shift-Aware Self-Supervised Learning. - Ngoc N. Tran; Son Duong; Hoang Phan; Tung Pham 001; Dinh Q. Phung; Trung Le 001 (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)

Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities. - Michael Fu; Trung Le 001; Van Nguyen 002; Chakkrit Tantithamthavorn; Dinh Q. Phung (CoRR, 2023)

RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization. - Tuan Truong; Hoang-Phi Nguyen; Tung Pham 001; Minh-Tuan Tran; Mehrtash Harandi; Dinh Phung 001; Trung Le 001 (CoRR, 2023)

Unleash Data Generation for Efficient and Effective Data-free Knowledge Distillation. - Minh-Tuan Tran; Trung Le 001; Xuan-May Le; Mehrtash Harandi; Quan Hung Tran; Dinh Q. Phung (CoRR, 2023)

Robust Contrastive Learning With Theory Guarantee. - Ngoc N. Tran; Lam Tran; Hoang Phan; Anh Tuan Bui; Tung Pham 001; Toan Tran 003; Dinh Q. Phung; Trung Le 001 (CoRR, 2023)

KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All. - Quyen Tran; Lam Tran; Khoat Than; Toan Tran 003; Dinh Q. Phung; Trung Le 001 (CoRR, 2023)

Class-Prototype Conditional Diffusion Model for Continual Learning with Generative Replay. - Khanh Doan; Quyen Tran; Tuan Nguyen 004; Dinh Q. Phung; Trung Le 001 (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)

VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types. - Michael Fu; Van Nguyen 002; Chakkrit Kla Tantithamthavorn; Trung Le 001; Dinh Q. Phung (IEEE Trans. Software Eng., 2023)

Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning. - Minh-Tuan Tran; Trung Le 001; Xuan-May Le; Mehrtash Harandi; Dinh Phung 001 (CVPR, 2024)

NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation. - Minh-Tuan Tran; Trung Le 001; Xuan-May Le; Mehrtash Harandi; Quan Hung Tran; Dinh Q. Phung (CVPR, 2024)

DiffAugment: Diffusion Based Long-Tailed Visual Relationship Recognition. - Parul Gupta; Tuan Nguyen 004; Abhinav Dhall; Munawar Hayat; Trung Le 001; Thanh-Toan Do (ECCV Workshops (20), 2024)

MetaAug: Meta-data Augmentation for Post-training Quantization. - Cuong Pham 007; Hoang Anh Dung; Cuong C. Nguyen; Trung Le 001; Dinh Phung 001; Gustavo Carneiro 001; Thanh-Toan Do (ECCV (27), 2024)

Preserving Generalization of Language models in Few-shot Continual Relation Extraction. - Quyen Tran; Nguyen Xuan Thanh; Nguyen Hoang Anh; Nam Le Hai; Trung Le 001; Linh Van Ngo 001; Thien Huu Nguyen (EMNLP, 2024)

Sharpness-Aware Data Generation for Zero-shot Quantization. - Hoang Anh Dung; Cuong Pham 007; Trung Le 001; Jianfei Cai 001; Thanh-Toan Do (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)

Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation. - Anh Bui; Tung-Long Vuong; Khanh Doan; Trung Le 001; Paul Montague; Tamas Abraham; Dinh Q. Phung (NeurIPS, 2024)

Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization. - Haocheng Luo; Tuan Truong; Tung Pham 001; Mehrtash Harandi; Dinh Q. Phung; Trung Le 001 (NeurIPS, 2024)

Enhancing Domain Adaptation through Prompt Gradient Alignment. - Viet Hoang Phan; Tung Lam Tran; Quyen Tran; Trung Le 001 (NeurIPS, 2024)

Frequency Attention for Knowledge Distillation. - Cuong Pham 007; Van-Anh Nguyen; Trung Le 001; Dinh Q. Phung; Gustavo Carneiro 001; Thanh-Toan Do (WACV, 2024)

EraseDiff: Erasing Data Influence in Diffusion Models. - Jing Wu 021; Trung Le 001; Munawar Hayat; Mehrtash Harandi (CoRR, 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)

Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts. - Anh Tuan Bui; Khanh Doan; Trung Le 001; Paul Montague; Tamas Abraham; Dinh Q. Phung (CoRR, 2024)

Diversity-Aware Agnostic Ensemble of Sharpness Minimizers. - Anh Bui; Vy Vo; Tung Pham 001; Dinh Q. Phung; Trung Le 001 (CoRR, 2024)

Agnostic Sharpness-Aware Minimization. - Van-Anh Nguyen; Quyen Tran; Tuan Truong; Thanh-Toan Do; Dinh Quoc Phung; Trung Le 001 (CoRR, 2024)

Enhancing Domain Adaptation through Prompt Gradient Alignment. - Hoang Phan; Lam Tran; Quyen Tran; Trung Le 001 (CoRR, 2024)

PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval. - Tuan-Luc Huynh; Thuy-Trang Vu; Weiqing Wang 001; Yinwei Wei; Trung Le 001; Dragan Gasevic; Yuan-Fang Li; Thanh-Toan Do (CoRR, 2024)

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. - Cuong Pham 007; Cuong C. Nguyen; Trung Le 001; Dinh Q. Phung; Gustavo Carneiro 001; Thanh-Toan Do (CoRR, 2024)

Connective Viewpoints of Signal-to-Noise Diffusion Models. - Khanh Doan; Long Tung Vuong; Tuan Nguyen 004; Anh Tuan Bui; Quyen Tran; Thanh-Toan Do; Dinh Phung 001; Trung Le 001 (CoRR, 2024)

Improving Generalization with Flat Hilbert Bayesian Inference. - Tuan Truong; Quyen Tran; Quan Pham-Ngoc; Nhat Ho; Dinh Phung 001; Trung Le 001 (CoRR, 2024)

Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning. - Quyen Tran; Minh Le; Tuan Truong; Dinh Phung 001; Linh Ngo Van 001; Thien Huu Nguyen; Nhat Ho; Trung Le 001 (CoRR, 2024)

Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation. - Anh Bui; Long Tung Vuong; Khanh Doan; Trung Le 001; Paul Montague; Tamas Abraham; Dinh Q. Phung (CoRR, 2024)

Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation. - Minh-Tuan Tran; Trung Le 001; Xuan-May Le; Jianfei Cai 001; Mehrtash Harandi; Dinh Q. Phung (CoRR, 2024)

AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities. - Michael Fu; Chakkrit Tantithamthavorn; Trung Le 001; Yuki Kume; Van Nguyen 002; Dinh Q. Phung; John C. Grundy (Empir. Softw. Eng., 2024)

Vision Transformer Inspired Automated Vulnerability Repair. - Michael Fu; Van Nguyen 002; Chakkrit Tantithamthavorn; Dinh Phung 001; Trung Le 001 (ACM Trans. Softw. Eng. Methodol., 2024)

Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection. - Van Nguyen 002; Trung Le 001; Chakkrit Tantithamthavorn; John C. Grundy; Dinh Q. Phung (ACM Trans. Softw. Eng. Methodol., 2024)

Erasing Undesirable Influence in Diffusion Models. - Jing Wu 021; Trung Le 001; Munawar Hayat; Mehrtash Harandi (CVPR, 2025)

Enhancing Dataset Distillation via Non-Critical Region Refinement. - Minh-Tuan Tran; Trung Le 001; Xuan-May Le; Thanh-Toan Do; Dinh Q. Phung (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 001; Dinh Phung 001 (CVPR, 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 001; Paul Montague; Tamas Abraham; Junae Kim; Dinh Phung 001 (ICLR, 2025)

Improved Training Technique for Latent Consistency Models. - Quan Dao; Khanh Doan; Di Liu 003; Trung Le 001; Dimitris N. Metaxas (ICLR, 2025)

Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts. - Minh Le; Chau Nguyen; Huy Nguyen; Quyen Tran; Trung Le 001; Nhat Ho (ICLR, 2025)

Boosting Multiple Views for pretrained-based Continual Learning. - Quyen Tran; Tung Lam Tran; Khanh Doan; Toan Tran 003; Dinh Q. Phung; Khoat Than; Trung Le 001 (ICLR, 2025)

Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction. - Nguyen Hoang Anh; Quyen Tran; Thanh Xuan Nguyen; Nguyen Thi Ngoc Diep; Linh Ngo Van 001; Thien Huu Nguyen; Trung Le 001 (NAACL (Long Papers), 2025)

DAViT: A Domain-Adapted Vision Transformer for Automated Pneumonia Detection and Explanation Using Chest X-Ray Images. - Michael Fu; Chakkrit Tantithamthavorn; Trung Le 001 (IEEE Access, 2025)

HVQ-VAE: Variational auto-encoder with hyperbolic vector quantization. - Shangyu Chen; Pengfei Fang; Mehrtash Harandi; Trung Le 001; Jianfei Cai 001; Dinh Q. Phung (Comput. Vis. Image Underst., 2025)

Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them. - Anh Bui; Trang Vu; Tung-Long Vuong; Trung Le 001; Paul Montague; Tamas Abraham; Junae Kim; Dinh Q. Phung (CoRR, 2025)

RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts. - Tuan Truong; Chau Nguyen; Huy Nguyen; Le Minh Nguyen 001; Trung Le 001; Nhat Ho (CoRR, 2025)

Why Domain Generalization Fail? A View of Necessity and Sufficiency. - Long Tung Vuong; Vy Vo; Hien Dang 004; Van-Anh Nguyen; Thanh-Toan Do; Mehrtash Harandi; Trung Le 001; Dinh Q. Phung (CoRR, 2025)

CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers. - Anh Duc Le; Tu Vu; Nam Le Hai; Nguyen Thi Ngoc Diep; Linh Ngo Van 001; Trung Le 001; Thien Huu Nguyen (CoRR, 2025)

Optimizing Specific and Shared Parameters for Efficient Parameter Tuning. - Van-Anh Nguyen; Thanh-Toan Do; Mehrtash Harandi; Dinh Q. Phung; Trung Le 001 (CoRR, 2025)

Exemplar-Free Continual Learning for State Space Models. - Isaac Ning Lee; Leila Mahmoodi; Trung Le 001; Mehrtash Harandi (CoRR, 2025)

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models. - Ngoc-Quan Pham; Tuan Truong; Quyen Tran; Tan Nguyen; Dinh Q. Phung; Trung Le 001 (CoRR, 2025)

Mitigating Semantic Collapse in Generative Personalization with a Surprisingly Simple Test-Time Embedding Adjustment. - Anh Bui; Trang Vu; Trung Le 001; Junae Kim; Tamas Abraham; Rollin Omari; Amar Kaur; Dinh Q. Phung (CoRR, 2025)

MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora. - Tuan-Luc Huynh; Thuy-Trang Vu; Weiqing Wang 001; Trung Le 001; Dragan Gasevic; Yuan-Fang Li; Thanh-Toan Do (CoRR, 2025)