Trung Le

发表

Graph-based Kernel Machine for Scalable Semi-supervised Learning

Dinh Q. Phung, Vu Nguyen, Khanh-Duy Nguyen, 2016 .

Mixture of Support Vector Data Descriptions

Khanh-Duy Nguyen, Trung Le, Vinh Lai, 2015, 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS).

On Deep Domain Adaptation: Some Theoretical Understandings

Dinh Q. Phung, Nhat Ho, Khanh-Duy Nguyen, 2018 .

Sparkling Vector Machines

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2015 .

On Transportation of Mini-batches: A Hierarchical Approach

Dinh Q. Phung, Nhat Ho, H. Bui, 2021, ICML.

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, ArXiv.

An Additive Instance-Wise Approach to Multi-class Model Interpretation

Quan Hung Tran, Vy A. Vo, Dinh Q. Phung, 2022, ArXiv.

An Additive Instance-Wise Approach to Multi-class Model Interpretation

Quan Hung Tran, Vy A. Vo, Dinh Q. Phung, 2022, ICLR.

Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation

Quan Hung Tran, Dinh Q. Phung, Trung Le, 2022, UAI.

Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle

John C. Grundy, Dinh Q. Phung, Trung Le, 2022, ArXiv.

Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?

Quan Hung Tran, Dinh Q. Phung, Thanh-Toan Do, 2022, ArXiv.

An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability

John C. Grundy, Dinh Q. Phung, S. Çamtepe, 2022, ArXiv.

Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations

Edwin V. Bonilla, Dinh Q. Phung, Gholamreza Haffari, 2022, ArXiv.

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, ArXiv.

Improving Multi-task Learning via Seeking Task-based Flat Regions

Lam C. Tran, Dinh Q. Phung, Nhat Ho, 2022, ArXiv.

GoGP: scalable geometric-based Gaussian process for online regression

Dinh Q. Phung, Vu Nguyen, Khanh-Duy Nguyen, 2018, Knowledge and Information Systems.

When Can Neural Networks Learn Connected Decision Regions? pdf

Trung Le, Dinh Q. Phung, Trung Le, 2019, ArXiv.

Global-Local Regularization Via Distributional Robustness

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, AISTATS.

Geometric Enclosing Networks

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, IJCAI.

Clustering Induced Kernel Learning

Nhan Dam, Trung Le, Tu Dinh Nguyen, 2018, ACML.

Multiple Kernel Learning with Data Augmentation

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, ACML.

Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web

Duc Thanh Nguyen, Dinh Q. Phung, Svetha Venkatesh, 2018, WISE.

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

Tuan Nguyen, Trung Le, Lizhen Qu, 2019, ICLR.

Approximation Vector Machines for Large-scale Online Learning

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, J. Mach. Learn. Res..

MGAN: Training Generative Adversarial Nets with Multiple Generators

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2018, ICLR.

Robust Anomaly Detection in Videos Using Multilevel Representations

Wei Luo, Trung Le, Dinh Q. Phung, 2019, AAAI.

VulRepair: a T5-based automated software vulnerability repair

Dinh Q. Phung, Trung Le, Van Nguyen, 2022, ESEC/SIGSOFT FSE.

Information-theoretic Source Code Vulnerability Highlighting

Dinh Phung, Van Nguyen, John Grundy, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Deep Domain Adaptation for Vulnerable Code Function Identification

Lizhen Qu, Dinh Q. Phung, Paul Montague, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance

Dinh Q. Phung, Trung Le, N. Tran, 2022, ArXiv.

Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning

Trung Le, Dinh Q. Phung, Seyit Camtepe, 2021, ArXiv.

Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning

Dinh Phung, Paul Montague, He Zhao, 2021, 2101.10027.

Dual Discriminator Generative Adversarial Nets

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, NIPS.

TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport

Quan Hung Tran, Nhan Dam, Trung Le, 2021, IJCAI.

LAMDA: Label Matching Deep Domain Adaptation

Trung Le, Dinh Phung, Nhat Ho, 2021, ICML.

Learning Generative Adversarial Networks from Multiple Data Sources

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2019, IJCAI.

Optimal Transport Model Distributional Robustness

Dinh Q. Phung, Thanh-Toan Do, Trung Le, 2023, ArXiv.

Flat Seeking Bayesian Neural Networks

Dinh Q. Phung, Thanh-Toan Do, Trung Le, 2023, ArXiv.

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

Quan Hung Tran, Dinh Q. Phung, Trung Le, 2022, ICLR.

Neural Topic Model via Optimal Transport

Trung Le, Dinh Phung, He Zhao, 2020, ICLR.

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, NeurIPS.

Continual Learning with Optimal Transport based Mixture Model

Dinh Q. Phung, Trung Le, Khoat Than, 2022, ArXiv.

Three-Player Wasserstein GAN via Amortised Duality

Nhan Dam, Trung Le, Tu Dinh Nguyen, 2019, IJCAI.

Most: multi-source domain adaptation via optimal transport for student-teacher learning

Quan Hung Tran, Dinh Phung, He Zhao, 2021, UAI.

One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems

Trung Le, Svetha Venkatesh, Tu Dinh Nguyen, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Parameterized Rate-Distortion Stochastic Encoder

Trung Le, Dinh Q. Phung, Dinh Phung, 2020, ICML.

On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds

Quan Hung Tran, Dinh Q. Phung, Paul Montague, 2022, AISTATS.

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

Quan Hung Tran, Dinh Q. Phung, Trung Le, 2021, 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

Trung Le, Dinh Q. Phung, Michael Papasimeon, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

Dinh Phung, Anh Tran, Trung Le, 2021, NeurIPS.

Improving Adversarial Robustness by Enforcing Local and Global Compactness

Trung Le, Dinh Q. Phung, Tamas Abraham, 2020, ECCV.

Multi-Generator Generative Adversarial Nets pdf

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, ArXiv.

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering pdf

Quan Hung Tran, Quan Tran, Nhan Dam, 2020, COLING.

Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection

Trung Le, John C. Grundy, Khanh Nguyen, 2020, PAKDD.

STEM: An approach to Multi-source Domain Adaptation with Guarantees

Quan Hung Tran, Dinh Q. Phung, Trung Le, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Improving kernel online learning with a snapshot memory

Dinh Q. Phung, Khanh-Duy Nguyen, Trung Le, 2022, Machine Learning.

Bayesian Multi-Hyperplane Machine for Pattern Recognition

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

Sparse Adaptive Multi-hyperplane Machine

Trung Le, Dinh Q. Phung, Khanh Nguyen, 2016, PAKDD.

Adaptable linear support vector machine

Khanh Nguyen, Trung Le, Van Nguyen, 2015, 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS).

Fast One-Class Support Vector Machine for Novelty Detection

Trung Le, Svetha Venkatesh, Dinh Q. Phung, 2015, PAKDD.

Nonparametric Budgeted Stochastic Gradient Descent

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, AISTATS.

Dual Space Gradient Descent for Online Learning

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, NIPS.

Large-scale Online Kernel Learning with Random Feature Reparameterization

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, IJCAI.

Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

Edwin V. Bonilla, Vy A. Vo, Dinh Q. Phung, 2022, KDD.

S TOCHASTIC O PTIMIZATION FOR C OUNTERFACTUAL E XPLANATIONS

Edwin V. Bonilla, Dinh Q. Phung, Gholamreza Haffari, 2023 .