no code implementations • 3 Nov 2023 • Qianxin Yi, Yiyang Yang, Shaojie Tang, Jiapeng Liu, Yao Wang
In this paper, we aim to build a novel bandits algorithm that is capable of fully harnessing the power of multi-dimensional data and the inherent non-linearity of reward functions to provide high-usable and accountable decision-making services.
no code implementations • 24 Sep 2021 • Chenhao Wang, Qianxin Yi, Xiuwu Liao, Yao Wang
Frequent Directions, as a deterministic matrix sketching technique, has been proposed for tackling low-rank approximation problems.
no code implementations • 23 Aug 2021 • Qianxin Yi, Chenhao Wang, Kaidong Wang, Yao Wang
Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data.