Huazheng Wang
Huazheng Wang
Assistant Professor, Oregon State University
Verified email at - Homepage
Cited by
Cited by
Contextual bandits in a collaborative environment
Q Wu, H Wang, Q Gu, H Wang
Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016
Factorization bandits for interactive recommendation
H Wang, Q Wu, H Wang
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
Learning hidden features for contextual bandits
H Wang, Q Wu, H Wang
Proceedings of the 25th ACM international on conference on information and …, 2016
Machine learning for synthetic data generation: a review
Y Lu, M Shen, H Wang, X Wang, C van Rechem, W Wei
arXiv preprint arXiv:2302.04062, 2023
Adversarial domain adaptation for machine reading comprehension
H Wang, Z Gan, X Liu, J Liu, J Gao, H Wang
arXiv preprint arXiv:1908.09209, 2019
Unbiased learning to rank: online or offline?
Q Ai, T Yang, H Wang, J Mao
ACM Transactions on Information Systems (TOIS) 39 (2), 1-29, 2021
Factorization bandits for online influence maximization
Q Wu, Z Li, H Wang, W Chen, H Wang
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
Variance reduction in gradient exploration for online learning to rank
H Wang, S Kim, E McCord-Snook, Q Wu, H Wang
Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019
Solving verbal comprehension questions in iq test by knowledge-powered word embedding
H Wang, F Tian, B Gao, J Bian, TY Liu
arXiv preprint arXiv:1505.07909, 2015
Efficient exploration of gradient space for online learning to rank
H Wang, R Langley, S Kim, E McCord-Snook, H Wang
The 41st international ACM SIGIR conference on research & development in …, 2018
Global and local differential privacy for collaborative bandits
H Wang, Q Zhao, Q Wu, S Chopra, A Khaitan, H Wang
Proceedings of the 14th ACM Conference on Recommender Systems, 150-159, 2020
Dynamic ensemble of contextual bandits to satisfy users' changing interests
Q Wu, H Wang, Y Li, H Wang
The World Wide Web Conference, 2080-2090, 2019
PARL: A unified framework for policy alignment in reinforcement learning
S Chakraborty, AS Bedi, A Koppel, D Manocha, H Wang, M Wang, ...
arXiv preprint arXiv:2308.02585, 3, 2023
Communication efficient distributed learning for kernelized contextual bandits
C Li, H Wang, M Wang, H Wang
Advances in Neural Information Processing Systems 35, 19773-19785, 2022
Pairrank: Online pairwise learning to rank by divide-and-conquer
Y Jia, H Wang, S Guo, H Wang
Proceedings of the web conference 2021, 146-157, 2021
Incentivized exploration for multi-armed bandits under reward drift
Z Liu, H Wang, F Shen, K Liu, L Chen
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4981-4988, 2020
Autodefense: Multi-agent llm defense against jailbreak attacks
Y Zeng, Y Wu, X Zhang, H Wang, Q Wu
arXiv preprint arXiv:2403.04783, 2024
When are linear stochastic bandits attackable?
H Wang, H Xu, H Wang
International Conference on Machine Learning, 23254-23273, 2022
Provable benefits of policy learning from human preferences in contextual bandit problems
X Ji, H Wang, M Chen, T Zhao, M Wang
arXiv preprint arXiv:2307.12975, 2023
Interactive information retrieval with bandit feedback
H Wang, Y Jia, H Wang
Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021
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