20 citations · 44 across the 8 of their papers we have counts for
8 papers
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Huazheng Wang, David Zhao, Hongning Wang
Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious pr…
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
Chuanhao Li, Huazheng Wang, Mengdi Wang +1
We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distribu…
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang
We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an adversary aims to control the behaviour of the bandit algorithm. Perhaps surprisingly, we…
Incentivizing Exploration in Linear Bandits under Information Gap
Huazheng Wang, Haifeng Xu, Chuanhao Li +2
We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In…
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Yiling Jia, Huazheng Wang, Stephen Guo +1
Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users. However, the required exp…
Unbiased Learning to Rank: Online or Offline?
Qingyao Ai, Tao Yang, Huazheng Wang +1
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) ca…
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
Zhiyuan Liu, Huazheng Wang, Fan Shen +2
We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide bi…
Adversarial Domain Adaptation for Machine Reading Comprehension
Huazheng Wang, Zhe Gan, Xiaodong Liu +3
In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled…