46 citations · 82 across the 13 of their papers we have counts for
13 papers
Dueling Bandits: From Two-dueling to Multi-dueling
Yihan Du, Siwei Wang, Longbo Huang
We study a general multi-dueling bandit problem, where an agent compares multiple options simultaneously and aims to minimize the regret due to selecting suboptimal arms. This sett…
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
Chen Zhang, Siwei Wang, Jiyuan Liu +5
Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to…
Pure Exploration Bandit Problem with General Reward Functions Depending on Full Distributions
Siwei Wang, Wei Chen
In this paper, we study the pure exploration bandit model on general distribution functions, which means that the reward function of each arm depends on the whole distribution, not…
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Chen Zhang, Siwei Wang, Wenxuan Tu +4
Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization whi…
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Mingjie Luo, Siwei Wang, Xinwang Liu +5
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dr…
Continuous Mean-Covariance Bandits
Yihan Du, Siwei Wang, Zhixuan Fang +1
Existing risk-aware multi-armed bandit models typically focus on risk measures of individual options such as variance. As a result, they cannot be directly applied to important rea…