3 citations · 12 across the 16 of their papers we have counts for
3 papers · 1 filter
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai +5
Large language models (LLMs) have shown remarkable instruction-following capabilities and achieved impressive performances in various applications. However, the performances of LLM…
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients
Yao Shu, Xiaoqiang Lin, Zhongxiang Dai +1
Federated optimization, an emerging paradigm which finds wide real-world applications such as federated learning, enables multiple clients (e.g., edge devices) to collaboratively o…
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…