Working Papers

  1. Regurgitative Training: The Value of Real Data in Training Large Language Models. Jinghui Zhang, Dandan Qiao, Mochen Yang, Qiang Wei. [SSRN] [ArXiv]
  2. What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning. Bowen Shi, Xiaojie Mao, Mochen Yang, Bo Li. [SSRN] [code]
  3. Human-AI Co-Creation in Product Ideation: the Dual View of Quality and Diversity. Wen Wang, Mochen Yang, Tianshu Sun. [SSRN]
  4. Algorithmic Governance via Recommender Systems: The Case of Short Video Platform. Jinghui Zhang, Mochen Yang, Xuan Bi, Qiang Wei. [SSRN]
  5. EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference. Gordon Burtch, Edward McFowland III, Mochen Yang, Gediminas Adomavicius. [ArXiv]

Publications

  1. Cost-Aware Calibration of Classifiers. Mochen Yang, Xuan Bi. INFORMS Journal on Data Science, forthcoming. [Journal Link] [SSRN]
  2. Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation. Xuan Bi, Alok Gupta, Mochen Yang. Management Science, 2023. [Journal Link] [SSRN]
  3. User Engagement on Social Media Business Pages: The Interplay between User Comments and Firm Responses. Xiaoye Cheng, Hillol Bala, Mochen Yang. MIS Quarterly, forthcoming.
  4. Judge Me on My Losers: Does Adaptive Robo-Advisors Outperform Human Investors during the COVID-19 Financial Market Crash? Che-Wei Liu, Mochen Yang, Ming-Hui Wen. Production and Operations Management, 2023. [Journal Link] [SSRN]
  5. Consumer Acquisitions for Recommender Systems: A Theoretical Framework and Empirical Evaluations. Xuan Bi, Mochen Yang, Gediminas Adomavicius. Information Systems Research, 2023. [Journal Link] [SSRN]
  6. Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective. Yash Travadi, Le Peng, Xuan Bi, Ju Sun, Mochen Yang. Statistics and Its Interface, forthcoming. [arXiv]
  7. When Algorithms Err: Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms. Antino Kim, Mochen Yang, Jingjing Zhang. ACM Transactions on Computer-Human Interaction (TOCHI), 2023. [Journal Link] [SSRN] [Presentation (by Antino Kim)]
  8. Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. Mochen Yang, Edward McFowland III, Gordon Burtch, Gediminas Adomavicius. INFORMS Journal on Data Science, 2022. [Journal Link] [SSRN] [Code] [Code Demo]
  9. Bidder Support in Multi-Item Multi-Unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework. Gediminas Adomavicius, Alok Gupta, Mochen Yang. Information Systems Research, 2022. [Journal Link] [SSRN]
  10. Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. Gediminas Adomavicius, Mochen Yang. ACM Transactions on Management Information Systems (TMIS), 2022. [Journal Link] [SSRN]
  11. Engagement by Design: An Empirical Study of the “Reactions” Feature on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. ACM Transactions on Computer-Human Interaction (TOCHI), 2020. [Journal Link] [SSRN] [Presentation]
  12. Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. Information Systems Research, 2019. [Journal Link] [SSRN] [Podcast]
  13. Efficient Computational Strategies for Dynamic Inventory Liquidation. Mochen Yang, Gediminas Adomavicius, Alok Gupta. Information Systems Research, 2019. [Journal Link] [SSRN]
  14. Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. Gediminas Adomavicius, Alok Gupta, Mochen Yang. MIS Quarterly, 2019. [Journal Link] [SSRN]
  15. Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Mochen Yang, Gediminas Adomavicius, Gordon Burtch, Yuqing Ren. Information Systems Research, 2018. [Journal Link] [SSRN]