2 citations · 3 across the 7 of their papers we have counts for
7 papers
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
Jize Xie, Haomiao Wu, Qiang Chen +2
Conditional generative models are increasingly used as scenario generators for stochastic optimization, but standard training objectives emphasize uniform distributional fit rather…
Cascading Bandits Robust to Adversarial Corruptions
Jize Xie, Cheng Chen, Zhiyong Wang +1
Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users' click feedback. In many real-world scenarios, user…
Online Clustering of Dueling Bandits
Zhiyong Wang, Jiahang Sun, Mingze Kong +4
The contextual multi-armed bandit (MAB) is a widely used framework for problems requiring sequential decision-making under uncertainty, such as recommendation systems. In applicati…
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
Zhiyong Wang, Jize Xie, Yi Chen +2
We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round. Existing algorithms characterize the non-stationarity by the to…
Online Corrupted User Detection and Regret Minimization
Zhiyong Wang, Jize Xie, Tong Yu +2
In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform…
Online Clustering of Bandits with Misspecified User Models
Zhiyong Wang, Jize Xie, Xutong Liu +2
The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in t…
Online Influence Maximization under Decreasing Cascade Model
Fang Kong, Jize Xie, Baoxiang Wang +2
We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the c…