38 citations · 66 across the 37 of their papers we have counts for
7 papers · 1 filter
Batch Bayesian Optimization for Replicable Experimental Design
Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay +4
Many real-world experimental design problems (a) evaluate multiple experimental conditions in parallel and (b) replicate each condition multiple times due to large and heteroscedas…
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…
Source Attribution for Large Language Model-Generated Data
Jingtan Wang, Xinyang Lu, Zitong Zhao +4
The impressive performances of Large Language Models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the Intellectual Property (IP…
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…
Dependency Structure Search Bayesian Optimization for Decision Making Models
Mohit Rajpal, Lac Gia Tran, Yehong Zhang +1
Many approaches for optimizing decision making models rely on gradient based methods requiring informative feedback from the environment. However, in the case where such feedback i…
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…