10 citations · 26 across the 13 of their papers we have counts for
13 papers
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…
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…
Data value estimation on private gradients
Zijian Zhou, Xinyi Xu, Daniela Rus +1
For gradient-based machine learning (ML) methods commonly adopted in practice such as stochastic gradient descent, the de facto differential privacy (DP) technique is perturbing th…
Data Distribution Valuation
Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo +2
Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a…
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…
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu +3
In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without up…
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…
Fair yet Asymptotically Equal Collaborative Learning
Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng +2
In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed…
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…
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergen…
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards
Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo +1
This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for trainin…
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
Xinyi Xu, Tiancheng Huang, Pengfei Wei +2
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristi…
Collaborative Fairness in Federated Learning
Lingjuan Lyu, Xinyi Xu, Qian Wang
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distr…