Wei Tang

About

I am an assistant professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong. Before this, I was a postdoctoral fellow at the Data Science Institute, Columbia University, mentored by Shipra Agrawal. I completed my Ph.D. in Computer Science at Washington University in St. Louis, where I was fortunate to be advised by Chien-Ju Ho. I received my bachelor's degree from Tianjin University, China.

I am interested in the theoretical aspects of agentic decision-making, where humans, algorithms, or both act as decision-makers. A typical question I explore is how to efficiently and effectively design or provide information and predictions to help humans and algorithms make desired decisions in complex environments.
My interests include sequential decision-making under uncertainty, reinforcement learning, information design, socially responsible machine learning, and human-AI interaction. My research spans machine learning, algorithmic economics, and behavioral and social sciences.

Prospective students: I am looking for students. If you are interested in working with me, please send me an email with your CV.

What's New?

• [12/2025]: I am attending NeurIPS 2025, San Diego. After that, I am attending WINE 2025, where we are organizing a tutorial on Information Design Perspective on Calibration.
• [10/2024]: Works to be presented at INFORMS 2024: dynamic pricing with long-term reference effects, rationality-robust information design, and dynamic pricing with Bayesian persuasion.
• [09/2024]: Check our new working paper on confusion matrix design for downstream decision-making and the recent NeurIPS 2024 work on the robustness of Prophet inequality to strategic reward signaling.

Working Papers

• Private Private Information in Second-Price Auction
  (α-β). Boyu Liu, Wei Tang, Zihe Wang, and Shuo Zhang.
• Prior-Agnostic Robust Forecast Aggregation
  (α-β). Zhi Chen, Cheng Peng, and Wei Tang.
• Simple and Robust Quality Disclosure: The Power of Quantile Partition
  (α-β). Shipra Agrawal, Yiding Feng, and Wei Tang.
• Pricing Query Complexity of Multiplicative Revenue Approximation
  (α-β). Wei Tang, Yifan Wang, and Mengxiao Zhang.
  To be Presented at ESIF-AIML 2026.
• Explainable Information Design
  (α-β). Yiling Chen, Tao Lin, Wei Tang, and Jamie Tucker-Foltz.
• Optimal Calibrated Signaling in Digital Auctions
  (α-β). Zhicheng Du, Wei Tang, Zihe Wang, and Shuo Zhang.
• Is This Predictor More Informative than Another? A Decision-Theoretical Comparison
  (α-β). Yiding Feng, Liuhan Qian, and Wei Tang.
  To be Presented at ESIF-AIML 2026.
• Simple Delay-Oblivious Policies Are Robust: Overbooking with Delayed Purchases
  (α-β). Yiding Feng, Puping Jiang, and Wei Tang.

Journal Paper

• No-Regret Bayesian Recommendation to Homogeneous Users
  (α-β) Yiding Feng, Wei Tang, and Haifeng Xu.
  Operations Research (OR) 2026.

Conference Papers

• Persuasive Calibration
  (α-β). Yiding Feng and Wei Tang.
  SODA 2026: The 37th ACM-SIAM Symposium on Discrete Algorithms. [conference version]
• No-Regret Online Autobidding Algorithms in First-price Auctions
  Yilin Li, Yuan Deng, Wei Tang, and Hanrui Zhang.
  NeurIPS 2025: The 39th Conference on Neural Information Processing Systems.
• Confusion Matrix Design for Downstream Decision-Making
  (α-β). Yiding Feng and Wei Tang.
  ITCS 2025: The 16th Innovations in Theoretical Computer Science.
• Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling
  (α-β). Wei Tang, Haifeng Xu, Ruimin Zhang, and Derek Zhu.
  NeurIPS 2024: The 38th Conference on Neural Information Processing Systems.
• Dynamic Pricing and Learning with Long-term Reference Effects
  (α-β). Shipra Agrawal and Wei Tang.
  EC 2024: The 25th ACM Conference on Economics and Computation.
  Major revision at Operations Research.
• Competitive Information Design with Asymmetric Senders
  (α-β). Zhicheng Du, Wei Tang, Zihe Wang, and Shuo Zhang.
  EC 2024: The 25th ACM Conference on Economics and Computation. [pdf]
• Performative Prediction with Bandit Feedback: Learning through Reparameterization
  Yatong Chen, Wei Tang, Chien-Ju Ho, and Yang Liu.
  ICML 2024: The 41st International Conference on Machine Learning.
• Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response
  (α-β). Yiding Feng, Chien-Ju Ho, and Wei Tang.
  SODA 2024: The 35th ACM-SIAM Symposium on Discrete Algorithms.
  Accepted for presentation at the 34th Stony Brook International Conference on Game Theory and the 2024 Behavioral Decision Research in Management conference.
• Dynamic Pricing and Advertising with Demand Learning
  (α-β). Shipra Agrawal, Yiding Feng, and Wei Tang.
  NeurIPS 2023: The 37th Conference on Neural Information Processing Systems. [talk by Shipra]
  Revise and resubmit, Management Science.
• Encoding Human Behavior in Information Design through Deep Learning
  Guanghui Yu, Wei Tang, Saumik Narayanan, and Chien-Ju Ho.
  NeurIPS 2023: The 37th Conference on Neural Information Processing Systems.
• Competitive Information Design for Pandora's Box
  (α-β). Bolin Ding, Yiding Feng, Chien-Ju Ho, Wei Tang, and Haifeng Xu.
  SODA 2023: The 34th ACM-SIAM Symposium on Discrete Algorithms.
• Online Bayesian Recommendation with No Regret
  (α-β). Yiding Feng, Wei Tang, and Haifeng Xu.
  EC 2022: The 23rd ACM Conference on Economics and Computation.
• How Does Predictive Information Affect Human Ethical Preferences?
  Saumik Narayanan, Guanghui Yu, Wei Tang, Chien-Ju Ho, and Ming Yin.
  AIES 2022: The 5th AAAI/ACM Conference on AI, Ethics, and Society.
• Bandit Learning with Delayed Impact of Actions
  Wei Tang, Chien-Ju Ho, and Yang Liu.
  NeurIPS 2021: The 35th Conference on Neural Information Processing Systems.
• On the Bayesian Rational Assumption in Information Design
  Wei Tang and Chien-Ju Ho.
  HCOMP 2021: The 9th AAAI Conference on Human Computation and Crowdsourcing.
  Best Paper Honorable Mention.
• Linear Models are Robust Optimal Under Strategic Behavior
  Wei Tang, Chien-Ju Ho, and Yang Liu.
  AISTATS 2021: The 24th International Conference on Artificial Intelligence and Statistics.
• Optimal Query Complexity of Secure Stochastic Convex Optimization
  Wei Tang, Chien-Ju Ho, and Yang Liu.
  NeurIPS 2020: The 34th Conference on Neural Information Processing Systems.
• Differentially Private Contextual Dynamic Pricing
  Wei Tang, Chien-Ju Ho, and Yang Liu.
  AAMAS 2020: The 19th International Conference on Autonomous Agents and Multiagent Systems. [full version] [slides]
• Leveraging Peer Communication to Enhance Crowdsourcing
  Wei Tang, Chien-Ju Ho, and Ming Yin.
  WWW 2019: The Web Conference. [poster]
• Bandit Learning with Biased Human Feedback
  Wei Tang and Chien-Ju Ho.
  AAMAS 2019: The 18th International Conference on Autonomous Agents and Multiagent Systems.
  Presented as a contributed talk at the first Workshop on Behavioral EC. [full version] [slides] [poster]
• Tumor origin detection with tissue-specific miRNA and DNA methylation markers
  Wei Tang, Shixiang Wan, Zhen Yang, Andrew E. Teschendorff, and Quan Zou.
  Bioinformatics, 2018.
• dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers
  Zhen Yang, Liangcai Wu, Anqiang Wang, Wei Tang, Yi Zhao, Haitao Zhao, and Andrew E. Teschendorff.
  Nucleic Acids Research, 2017.
• Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis
  Wei Tang, Zhijun Liao, and Quan Zou.
  Oncotarget, 2016.
• ... Show more

Tutorial / Service

• Tutorials
  • [EC 2026] Information Design Perspective on Calibration
  • [WINE 2025] Information Design Perspective on Calibration: [Slides Part I]; [Slides Part II].
• Conference reviewers
  • 2026: STOC, ICML.
  • 2025: SODA, AISTATS, ICML, EC (PC), NeurIPS, WINE (PC).
  • 2024: ICML, EC (PC), NeurIPS, SODA, ISAAC.
  • 2023: FAccT, ICML, NeurIPS.
  • 2022: ICLR, ICML, FAccT, UAI, SAGT, NeurIPS, WINE.
  • 2021: AAMAS, AAAI, AISTATS, NeurIPS.
• Journal referee
  • Journal of Machine Learning Research, Machine Learning Journal, Transactions on Machine Learning Research, ACM Transactions on Economics and Computation, Autonomous Agents and Multi-Agent Systems
  • Management Science, Operations Research, Production and Operations Management, Manufacturing & Service Operations Management,
  • Games and Economic Behavior.
  • Journal of the American Statistical Association.

Misc.

It has also been my good fortune to work with Yatong Chen, Bolin Ding, Yiding Feng, Puping Jiang, Yang Liu, Saumik Narayanan, Zihe Wang, Haifeng Xu, Ming Yin, and Guanghui Yu.