33 citations · 35 across the 13 of their papers we have counts for
14 papers
How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
Jiangwei Chen, Xinyuan Niu, Rachael Hwee Ling Sim +3
Machine unlearning aims to remove the influence of specific forget training data due to privacy, copyright or bias concerns while maintaining the model performance on the remaining…
Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
Rachael Hwee Ling Sim, Jue Fan, Xiao Tian +3
Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods…
Is Data Shapley Not Better than Random in Data Selection? Ask NASH
Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +3
Data selection studies the problem of identifying high-quality subsets of training data. While some existing works have considered selecting the subset of data with top- Data Sh…
INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +1
Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owners gain greater control over th…
De-attribute to Forget for LLM Unlearning
Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim +3
The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many…
Incentivizing Time-Aware Fairness in Data Sharing
Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim +4
In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the…
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou +15
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented compl…
WaterDrum: Watermarking for Data-centric Unlearning Metric
Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau +7
Large language model (LLM) unlearning is critical in real-world applications where it is necessary to efficiently remove the influence of private, copyrighted, or harmful data from…
DUPRE: Data Utility Prediction for Efficient Data Valuation
Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Quoc Phong Nguyen +2
Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperat…
Data-Centric AI in the Age of Large Language Models
Xinyi Xu, Zhaoxuan Wu, Rui Qiao +16
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making the key observation that data is instrumental in…
Incentives in Private Collaborative Machine Learning
Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang +3
Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and rew…
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning
Xiao Tian, Rachael Hwee Ling Sim, Jue Fan +1
Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for pr…
Probably Approximate Shapley Fairness with Applications in Machine Learning
Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim +2
The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness…
Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan +1
Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML models by training on the aggregated data from many parties. However, these parties are only w…