* Equal contribution (alphabetical order)

Journal Publications

• R Gao, M Yin, J McInerney, N Kallus. Adjusting Regression Models for Conditional Uncertainty Calibration. Machine Learning (Special Issue on Uncertainty Quantification), 2024.

Papers Under Review or Revision at Journals

• R Gao, M De-Arteaga, M Saar-Tsechansky. Learning Complementary Policies for Human-AI Deferral Collaboration. Major revision at Management Science. (Previous title: Robust Human-AI Collaboration with Bandit Feedback. Best Student Paper at CIST 2022.)

• Y Yang, R Gao, Z Zheng. Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption. Major revision at Management Science. (Preliminary version at CIST 2025 and LockLLM @ NeurIPS 2025. Best Paper at INFORMS Workshop on Data Science 2025, Best Student Paper at WITS 2025.)

• J Cao*, R Gao*, E Keyvanshokooh*, J Ma*. LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning. Under review at Manufacturing & Service Operations Management.

• M Biggs*, R Gao*, W Sun*. Loss Functions for Discrete Contextual Pricing with Observational Data. Under review at Manufacturing & Service Operations Management. (Spotlight presentation at INFORMS RMP 2022, Special Recognition Award Finalist at INFORMS ADA 2022.)

• M Yin, R Gao, Z Cong. Generative Personalization with Textual and Non-textual Data. Under review at Marketing Science (resubmitted after reject-and-resubmit). (Previous title: Personalizing Language Models for Generative Targeting. Supported by a Marketing Science Institute grant.)

• Z Bian*, M Biggs*, R Gao*, Z Qi*. Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights. Under review at Operations Research.

• J Deng, X Jiang, S Zhang, S Zhang, H Lakkaraju, R Gao, C Donahue, JW Ma. Computational Copyright: Towards a Royalty Model for Music Generative AI. Under review at Manufacturing & Service Operations Management. (Preliminary version at the ICML 2024 Workshop on Generative AI and Law.)

Publications at ML/AI Conferences

• J Cao*, R Gao*, E Keyvanshokooh*. HR-Bandit: Human-AI Collaborated Linear Recourse Bandit. AISTATS 2025. (Preliminary version at CIST 2023.)

• R Gao, M Yin. Confounding-Robust Deferral Policy Learning. AAAI 2025. (Preliminary version at INFORMS Data Science Workshop 2023.)

• R Gao, M Yin, M Saar-Tsechansky. SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection. NeurIPS 2024. (Best Paper Runner-Up at WITS 2024.)

• R Gao, H Lakkaraju. On the Impact of Algorithmic Recourse on Social Segregation. ICML 2023.

• Z Wang*, R Gao*, M Yin*, M Zhou, D Blei. Probabilistic Conformal Prediction Using Conditional Random Samples. AISTATS 2023. (Preliminary version as spotlight presentation at ICML DFUQ 2022.)

• R Gao, M Biggs, W Sun, L Han. Enhancing Counterfactual Classification Performance via Self-Training. AAAI 2022.

• L Han, MR Min, A Stathopoulos, Y Tian, R Gao, A Kadav, D Metaxas. Dual Projection Generative Adversarial Networks for Conditional Image Generation. ICCV 2021.

• R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, M Lease. Human-AI Collaboration with Bandit Feedback. IJCAI 2021.

• R Gao, M Saar-Tsechansky. Cost-Accuracy Aware Adaptive Labeling for Active Learning. AAAI 2020.

• L Han, R Gao, M Kim, X Tao, B Liu, D Metaxas. Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons. AAAI 2020.

• L Han, Y Zou, R Gao, L Wang, D Metaxas. Unsupervised Domain Adaptation via Calibrating Uncertainties. CVPR Workshops 2019.

Working Papers

• Z Chen*, R Gao*, Y Liang*. Revealing AI Reasoning Increases Trust but Crowds Out Unique Human Knowledge. (Preliminary version presented at WISE 2025.)

• R Gao, SC Xiao. Nonstandard Errors in AI Agents. (Presented at TSWIM 2026.)

• R Gao, H Pang. Ninety-Eight Percent Talk: Digital Twins of the FOMC and Monetary Policy Surprises. (Preliminary version presented at SCECR 2026.)

• MJ Lee, R Gao, E Keyvanshokooh. Sequential Feature Acquisition for Treatment Assignment under Partial Observability. (Accepted at INFORMS Workshop on Data Science 2026.)

• Y Yang, R Gao, Z Zheng. Insight without Sight: LLM Inference with Private Data. (Accepted at INFORMS Workshop on Data Science 2026.)

• Z Qing, R Gao, M Yin. Evaluating Agents When Experts Know More: A Doubly-Valid and Doubly-Sharp Approach. (Accepted at INFORMS Workshop on Data Science 2026.)

Patents

• R Gao, W Sun, M Biggs, M Ettl, Y Drissi. Counterfactual Self-Training. US Patent App. 17/402,367.

• R Gao, W Sun, M Biggs, Y Drissi, M Ettl. Imputing Counterfactual Data to Facilitate Machine Learning Model Training. US Patent App. 17/654,617.