Sim, Rachael Hwee Ling

10 publications

ICLR 2026 How to Cure Newton for Unlearning Neural Networks? an Empirical Study from the Hessian Perspective Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim, See-Kiong Ng, Bryan Kian Hsiang Low

ICLR 2026 INO-SGD: Addressing Utility Imbalance Under Individualized Differential Privacy Xiao Tian, Jue Fan, Rachael Hwee Ling Sim, Bryan Kian Hsiang Low

ICLR 2026 WaterDrum: Watermark-Based Data-Centric Unlearning Metric Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau, Nhung Bui, Rachael Hwee Ling Sim, John Russell Himawan, Fanyu Wen, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low

NeurIPS 2025 Incentivizing Time-Aware Fairness in Data Sharing Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Arun Verma, Zhaoxuan Wu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

AAAI 2024 DeRDaVa: Deletion-Robust Data Valuation for Machine Learning Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low

ICML 2024 Deletion-Anticipative Data Selection with a Limited Budget Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low

AAAI 2023 Probably Approximate Shapley Fairness with Applications in Machine Learning Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Bryan Kian Hsiang Low

IJCAI 2022 Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low

ICML 2021 Collaborative Bayesian Optimization with Fair Regret Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet

ICML 2020 Collaborative Machine Learning with Incentive-Aware Model Rewards Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low