Inverse Reinforcement Learning with Locally Consistent Reward Functions. - Quoc Phong Nguyen; Kian Hsiang Low; Patrick Jaillet (NIPS, 2015)

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. - Quoc Phong Nguyen; Kar Wai Lim; Dinil Mon Divakaran; Kian Hsiang Low; Mun Choon Chan (CNS, 2019)

Inverse Reinforcement Learning with Missing Data. - Tien Mai; Quoc Phong Nguyen; Kian Hsiang Low; Patrick Jaillet (CoRR, 2019)

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. - Sreejith Balakrishnan; Quoc Phong Nguyen; Bryan Kian Hsiang Low; Harold Soh (NeurIPS, 2020)

Variational Bayesian Unlearning. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2020)

An Information-Theoretic Framework for Unifying Active Learning Problems. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (AAAI, 2021)

Top-k Ranking Bayesian Optimization. - Quoc Phong Nguyen; Sebastian Tay; Bryan Kian Hsiang Low; Patrick Jaillet (AAAI, 2021)

Value-at-Risk Optimization with Gaussian Processes. - Quoc Phong Nguyen; Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2021)

Optimizing Conditional Value-At-Risk of Black-Box Functions. - Quoc Phong Nguyen; Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2021)

Learning to learn with Gaussian processes. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (UAI, 2021)

Trusted-maximizers entropy search for efficient Bayesian optimization. - Quoc Phong Nguyen; Zhaoxuan Wu; Bryan Kian Hsiang Low; Patrick Jaillet (UAI, 2021)

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten. - Quoc Phong Nguyen; Ryutaro Oikawa; Dinil Mon Divakaran; Mun Choon Chan; Bryan Kian Hsiang Low (AsiaCCS, 2022)

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2022)

Rectified Max-Value Entropy Search for Bayesian Optimization. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (CoRR, 2022)

No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. - Sebastian Shenghong Tay; Quoc Phong Nguyen; Chuan Sheng Foo; Bryan Kian Hsiang Low (AISTATS, 2023)

Batch Bayesian Optimization For Replicable Experimental Design. - Zhongxiang Dai; Quoc Phong Nguyen; Sebastian Tay; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2023)

Optimistic Bayesian Optimization with Unknown Constraints. - Quoc Phong Nguyen; Wan Theng Ruth Chew; Le Song; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2024)

Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2024)

Active Set Ordering. - Quoc Phong Nguyen; Sunil Gupta 001; Svetha Venkatesh; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2024)

DUPRE: Data Utility Prediction for Efficient Data Valuation. - Kieu Thao Nguyen Pham; Rachael Hwee Ling Sim; Quoc Phong Nguyen; See-Kiong Ng; Bryan Kian Hsiang Low (AAMAS, 2025)

BILBO: BILevel Bayesian Optimization. - Wan Theng Ruth Chew; Quoc Phong Nguyen; Bryan Kian Hsiang Low (CoRR, 2025)