* Equal Contribution

Selected Publications at ML/AI Conferences

• R Gao, M Yin, M Saar-Tsechansky. SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection (NeurIPS, 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 accepted as spotlight presentation at ICML DFUQ, 2022)

• R Gao, M Biggs, W Sun, and L Han. Enhancing counterfactual classification performance via self-training (AAAI 2022)

• 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 Workshop 2019)

• 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)

Journal Publications and Under Review at Journals

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

• [Under Review] R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, W Sun, M Lease. Learning Complementary Policies for Human-AI Teams (Under review at Management Science - Reject and Resubmit, previous title: Robust Human-AI Collaboration with Bandit Feedback, Best Student Paper at CIST, 2022)

• [Under Review] M Biggs*, R Gao*, W Sun* Loss Functions for Discrete Contextual Pricing with Observational Data (Under review at Operations Research - Major Revision, spotlight presentation at RMP 2022, special recognition award finalist at ADA 2022).

Patent

• 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.

Working Journal Papers

Manuscript will be shared upon request.

• R Gao, M Saar-Tsechansky. Active Incentive Learning (Preliminary version accepted at CIST, 2022) [Abstract]

• R Gao, M Yin. Confounding-Robust Policy Improvement with Human-AI Teams (Preliminary version accepted at INFORMS Data Science Workshop, 2023) [Abstract]

• J Cao*, R Gao*, E Keyvanshokooh*, Contextual Recourse Bandits: Optimizing Decisions through Counterfactual Explanations (Preliminary version accepted at CIST, 2023) [Abstract]