Huazheng Wang

Assistant Professor, School of Electrical Engineering and Computer Science, Oregon State University Email: huazheng.wang [at] oregonstate.edu

About me

I am an Assistant Professor in the School of Electrical Engineering and Computer Science (EECS) at Oregon State University. I was a Postdoctoral Research Associate at the Department of Electrical and Computer Engineering at Princeton University from 2021 to 2022, hosted by Dr. Mengdi Wang. I received my Ph.D. in Computer Science at University of Virginia in 2021, supervised by Dr. Hongning Wang. I received my B.Eng. in Computer Science at University of Science and Technology of China in 2015. My research interests include reinforcement learning, information retrieval and machine learning in general. Currently I focus on multi-armed bandits and reinforcement learning with application to online recommendation and other information retrieval problems.

Updates: I am looking for self-motivated PhD students with solid math and coding backgrounds starting Fall 2024. More information can be found here for prospective students.

News and Updates

• [09/2023] One paper on offline RL for learning to rank is accepted by NeurIPS 2023.

• [04/2023] One paper on representation learning in POMDP is accepted by ICML 2023. See you in Hawaii!

• [01/2023] Our asynchronous kernel bandits paper is accepted by ICLR 2023.

• [09/2022] Two papers accepted by NeurIPS 2022: one on distributed kernel bandits and the other on Thompson Sampling for Directed Evolution.

• [09/2022] Joined EECS at Oregon State University as an Assistant Professor!

Honors and Awards

• [08/2019], SIGIR 2019 Best Paper Award.

• [2018 - 2021], Bloomberg Data Science Ph.D. Fellowship.

• [08/2021], ICML 2021 Best Reviewers (Top 10%).

Publications

• Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
  Zeyu Zhang, Yi Su, Hui Yuan, Yiran Wu, Rishab Balasubramanian, Qingyun Wu, Huazheng Wang, Mengdi Wang. NeurIPS 2023. [arXiv]

• Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
  Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang. International Conference on Machine Learning (ICML 2023). [arXiv]

• Incentivizing Exploration in Linear Bandits under Information Gap
  Huazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu, Hongning Wang. Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023). [arXiv]

• Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment
  Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang. The Eleventh International Conference on Learning Representations (ICLR 2023). [paper]

• Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
  Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). [arXiv]

• Communication Efficient Distributed Learning for Kernelized Contextual Bandits
  Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). [arXiv]

• Dynamic Global Sensitivity for Differentially Private Contextual Bandits
  Huazheng Wang, David Zhao, Hongning Wang. Proceedings of the 16th ACM Conference on Recommender Systems (RecSys 2022). [arXiv]

• When Are Linear Stochastic Bandits Attackable?
  Huazheng Wang, Haifeng Xu, Hongning Wang. International Conference on Machine Learning (ICML 2022). [arXiv]

• PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
  Yiling Jia, Huazheng Wang, Stephen Guo, Hongning Wang, Proceedings of the Web Conference 2021 (WWW 2021). Nominated for the Best Paper Award [arXiv] [code]

• Global and Local Differential Privacy for Collaborative Bandits
  Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang, Fourteenth ACM Conference on Recommender Systems (RecSys 2020). [pdf]

• Unbiased Learning to Rank: Online or Offline?
  Qingyao Ai, Tao Yang, Huazheng Wang, Jiaxin Mao, ACM Transactions on Information Systems (TOIS). [arXiv] [code]

• A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandits Problem
  Zhiyuan Liu, Huazheng Wang, Bo Waggoner, Youjian(Eugene) Liu, Lijun Chen, Workshop on Real World Experiment Design and Active Learning at ICML 2020. [arXiv]

• Incentivized Exploration for Multi-Armed Bandits under Reward Drift
  Zhiyuan Liu*, Huazheng Wang*, Fan Shen, Kai Liu and Lijun Chen, The 34th AAAI Conference on Artifical Intelligence (AAAI 2020). [arXiv]

• Adversarial Domain Adaptation for Machine Reading Comprehension
  Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Hongning Wang, (EMNLP 2019). [arXiv]

• Variance Reduction in Gradient Exploration for Online Learning to Rank
  Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, Hongning Wang, The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019). Best Paper Award [arXiv] [code]

• Factorization Bandits for Online Influence Maximization
  Qingyun Wu, Zhige Li, Huazheng Wang, Wei Chen, Hongning Wang, The 25th ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD 2019). [arXiv] [code]

• Dynamic Ensemble of Contextual Bandits to Satisfy Users’ Changing Interests
  Qingyun Wu, Huazheng Wang, Yanen Li, Hongning Wang, The Web Conference 2019 (WWW 2019). [pdf] [code]

• Efficient Exploration of Gradient Space for Online Learning to Rank
  Huazheng Wang, Ramsey Langley, Sonwoo Kim, Eric McCord-Snook, Hongning Wang, The 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). [arXiv] [code]

• Factorization Bandits for Interactive Recommendation
  Huazheng Wang, Qingyun Wu, Hongning Wang, The 31st AAAI Conference on Artifical Intelligence (AAAI 2017). [pdf] [Supplementary] [code]

• Learning Hidden Features for Contextual Bandits
  Huazheng Wang, Qingyun Wu, Hongning Wang, The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016). [pdf] [code]

• Contextual Bandits in A Collaborative Environment
  Qingyun Wu, Huazheng Wang, Quanquan Gu, Hongning Wang, The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016). [pdf] [code]

• Solving Verbal Comprehension Problems in IQ Test by Knowledge-Powered Word Embedding
  Huazheng Wang, Fei Tian, Bin Gao, Chengjieren Zhu, Jiang Bian, Tie-Yan Liu, Conference on Empirical Methods in Natural Language Processing, 2016 (EMNLP-16). [arXiv] [data]

Preprints

• Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
  Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song, Mengdi Wang, Huazheng Wang [arXiv]

• Adversarial Attacks on Combinatorial Multi-Armed Bandits
  Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao (Alphabetic order). [arXiv]

• PARL: A Unified Framework for Policy Alignment in Reinforcement Learning
  Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Dinesh Manocha, Huazheng Wang, Furong Huang, Mengdi Wang [arXiv]

• Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
  Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang. [arXiv]

• Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
  Kaixuan Huang, Yu Wu, Xuezhou Zhang, Shenyinying Tu, Qingyun Wu, Mengdi Wang, Huazheng Wang. [arXiv]

Tutorials

• Interactive Information Retrieval with Bandit Feedback
  Huazheng Wang, Yiling Jia, Hongning Wang, SIGIR 2021. [Website] [Slides]

• Learning by Exploration: New Challenges in Real-World Environments
  Qingyun Wu, Huazheng Wang, Hongning Wang, KDD 2020. [Website] [Slides]