On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. - Pu Zhao 001; Sijia Liu 001; Pin-Yu Chen; Nghia Hoang; Kaidi Xu; Bhavya Kailkhura; Xue Lin 001 (ICCV, 2019)

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. - Quang Minh Hoang; Nghia Hoang; Hai Pham; David P. Woodruff (NeurIPS, 2020)

Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges. - Nathan Fulton; Nathan Hunt; Nghia Hoang; Subhro Das (CoRR, 2020)

Verifiably Safe Exploration for End-to-End Reinforcement Learning. - Nathan Hunt; Nathan Fulton; Sara Magliacane; Nghia Hoang; Subhro Das; Armando Solar-Lezama (CoRR, 2020)

Incentives in Private Collaborative Machine Learning. - Rachael Hwee Ling Sim; Yehong Zhang; Nghia Hoang; Xinyi Xu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2023)

Pre-trained Recommender Systems: A Causal Debiasing Perspective. - Ziqian Lin; Hao Ding 003; Nghia Hoang; Branislav Kveton; Anoop Deoras; Hao Wang 014 (CoRR, 2023)

Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques. - Cuong Dao; Phi Le Nguyen; Truong Thao Nguyen; Nghia Hoang (NeurIPS, 2024)