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

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. - Sebastian Shenghong Tay; Xinyi Xu; Chuan Sheng Foo; Bryan Kian Hsiang Low (AAAI, 2022)

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. - Sebastian Shenghong Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICML, 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)

Bayesian Optimization with Cost-varying Variable Subsets. - Sebastian Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (NeurIPS, 2023)

A Unified Framework for Bayesian Optimization under Contextual Uncertainty. - Sebastian Shenghong Tay; Chuan-Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICLR, 2024)