4 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
A Unified Regularization Approach to High-Dimensional Generalized Tensor Bandits
Jiannan Li, Yiyang Yang, Yao Wang +1
Modern decision-making scenarios often involve data that is both high-dimensional and rich in higher-order contextual information, where existing bandits algorithms fail to generat…
Efficient Generalized Low-Rank Tensor Contextual Bandits
Qianxin Yi, Yiyang Yang, Shaojie Tang +2
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…
Lifting the Veil: Unlocking the Power of Depth in Q-learning
Shao-Bo Lin, Tao Li, Shaojie Tang +2
With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great succ…
Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees
Kaidong Wang, Qianxin Yi, Yao Wang +3
We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limita…
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration
Chenhao Wang, Qianxin Yi, Xiuwu Liao +1
Frequent Directions, as a deterministic matrix sketching technique, has been proposed for tackling low-rank approximation problems. This method has a high degree of accuracy and pr…
Effective Streaming Low-tubal-rank Tensor Approximation via Frequent Directions
Qianxin Yi, Chenhao Wang, Kaidong Wang +1
Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data. However, finding such an accurate approximation is challenging in the strea…