Quantum Bayesian Optimization. - Zhongxiang Dai; Gregory Kang Ruey Lau; Arun Verma; Yao Shu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2023)

Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. - Gregory Kang Ruey Lau; Xinyuan Niu 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (EMNLP, 2024)

Position Paper: Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (EMNLP (Findings), 2024)

PINNACLE: PINN Adaptive ColLocation and Experimental points selection. - Gregory Kang Ruey Lau; Apivich Hemachandra; See-Kiong Ng; Bryan Kian Hsiang Low (ICLR, 2024)

Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (CoRR, 2024)

Waterfall: Framework for Robust and Scalable Text Watermarking. - Gregory Kang Ruey Lau; Xinyuan Niu 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2024)

Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks. - Gregory Kang Ruey Lau; Wenyang Hu; Diwen Liu; Jizhuo Chen; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2024)

PIED: Physics-Informed Experimental Design for Inverse Problems. - Apivich Hemachandra; Gregory Kang Ruey Lau; See-Kiong Ng; Bryan Kian Hsiang Low (ICLR, 2025)

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks. - Zhiliang Chen; Gregory Kang Ruey Lau; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2025)

WaterDrum: Watermarking for Data-centric Unlearning Metric. - Xinyang Lu; Xinyuan Niu 001; Gregory Kang Ruey Lau; Bui Thi Cam Nhung; Rachael Hwee Ling Sim; Fanyu Wen; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2025)