38 citations · 66 across the 37 of their papers we have counts for
8 papers · 1 filter
Paid with Models: Optimal Contract Design for Collaborative Machine Learning
Bingchen Wang, Zhaoxuan Wu, Fusheng Liu +1
Collaborative machine learning (CML) provides a promising paradigm for democratizing advanced technologies by enabling cost-sharing among participants. However, the potential for r…
Self-Interested Agents in Collaborative Machine Learning: An Incentivized Adaptive Data-Centric Framework
Nithia Vijayan, Bryan Kian Hsiang Low
We propose a framework for adaptive data-centric collaborative machine learning among self-interested agents, coordinated by an arbiter. Designed to handle the incremental nature o…
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Yao Shu, Wenyang Hu, See-Kiong Ng +2
Large Language Models (LLMs) have become indispensable in numerous real-world applications. However, fine-tuning these models at scale, especially in federated settings where data…
Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies
Arun Verma, Indrajit Saha, Makoto Yokoo +1
This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an indivisible item that must be irrevoc…
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…
On Newton's Method to Unlearn Neural Networks
Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim +2
With the widespread applications of neural networks (NNs) trained on personal data, machine unlearning has become increasingly important for enabling individuals to exercise their…
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai +5
Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream task…
Localized Zeroth-Order Prompt Optimization
Wenyang Hu, Yao Shu, Zongmin Yu +5
The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of…