Jeffreys prior regularization for logistic regression. - Tam Nguyen; Raviv Raich; Phung Lai (SSP, 2016)

Convmd: Convolutive Matrix Decomposition For Classification Of Matrix Data. - Phung Lai; Raviv Raich; Molly Megraw (SSP, 2018)

Ontology-based Interpretable Machine Learning for Textual Data. - Phung Lai; NhatHai Phan; Han Hu 007; Anuja Badeti; David Newman; Dejing Dou (IJCNN, 2020)

A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis. - Trung Vu 001; Phung Lai; Raviv Raich; Anh T. Pham 001; Xiaoli Z. Fern; Arvind U. K. Rao (IEEE Trans. Medical Imaging, 2020)

Continual Learning with Differential Privacy. - Pradnya Desai; Phung Lai; NhatHai Phan; My T. Thai (ICONIP (6), 2021)

User-Entity Differential Privacy in Learning Natural Language Models. - Phung Lai; NhatHai Phan; Tong Sun 005; Rajiv Jain; Franck Dernoncourt; Jiuxiang Gu; Nikolaos Barmpalios (IEEE Big Data, 2022)

Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks. - Khang Tran; Phung Lai; NhatHai Phan; Issa Khalil; Yao Ma 001; Abdallah Khreishah; My T. Thai; Xintao Wu (IEEE Big Data, 2022)

Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning. - Phung Lai; Han Hu 007; Hai Phan 001; Ruoming Jin; My T. Thai; An M. Chen (CoLLAs, 2022)

Model Transferring Attacks to Backdoor HyperNetwork in Personalized Federated Learning. - Phung Lai; NhatHai Phan; Abdallah Khreishah; Issa Khalil; Xintao Wu (CoRR, 2022)

OnML: an ontology-based approach for interpretable machine learning. - Pelin Ayranci; Phung Lai; NhatHai Phan; Han Hu 007; Alexander Kolinowski; David Newman; Deijing Dou (J. Comb. Optim., 2022)

XRand: Differentially Private Defense against Explanation-Guided Attacks. - Truc D. T. Nguyen; Phung Lai; Hai Phan 001; My T. Thai (AAAI, 2023)

Active Membership Inference Attack under Local Differential Privacy in Federated Learning. - Truc D. T. Nguyen; Phung Lai; Khang Tran; NhatHai Phan; My T. Thai (AISTATS, 2023)

Differential Privacy in HyperNetworks for Personalized Federated Learning. - Vaisnavi Nemala; Phung Lai; NhatHai Phan (CIKM, 2023)

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models. - Thilanka Munasinghe; Phung Lai; Jennifer C. Wei; James A. Hendler; Kimberly A. Cornell (IEEE Big Data, 2024)

XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution. - Kiana Vu; Phung Lai; Truc Nguyen (IEEE Big Data, 2024)

Navigating Trustworthiness in LLMs: An Examination of Privacy, Security, and Robustness. - Kieu Dang; Phung Lai (CSoNet, 2024)

Trustworthiness in Vision-Language Models. - Kiana Vu; Phung Lai (CSoNet, 2024)

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps. - Xiaopeng Jiang; Han Hu 007; Thinh On; Phung Lai; Vijaya Datta Mayyuri; An M. Chen; Devu M. Shila; Adriaan Larmuseau; Ruoming Jin; Cristian Borcea; NhatHai Phan (IEEE Trans. Mob. Comput., 2024)

FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning. - Phung Lai; Xiaopeng Jiang; Hai Phan 001; Cristian Borcea; Khang Tran; An M. Chen; Vijaya Datta Mayyuri; Ruoming Jin (CoRR, 2025)

A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning. - Phung Lai; Guanxiong Liu; NhatHai Phan; Issa Khalil; Abdallah Khreishah; Xintao Wu (CoRR, 2025)

SoK: Are Watermarks in LLMs Ready for Deployment? - Kieu Dang; Phung Lai; NhatHai Phan; Yelong Shen; Ruoming Jin; Abdallah Khreishah; My T. Thai (CoRR, 2025)