Ruijiang Gao

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Ligong HanRed Hat AI, MIT-IBM Watson AI Lab, Rutgers UniversityCorreu electrònic verificat a mit.edu
Dimitris N. MetaxasBoard of Governors and Distinguished Professor of Computer Science, Rutgers UniversityCorreu electrònic verificat a cs.rutgers.edu
Maytal Saar-TsechanskyThe University of Texas at AustinCorreu electrònic verificat a mccombs.utexas.edu
Max BiggsAssistant Professor, Darden School of Business, University of VirginiaCorreu electrònic verificat a darden.virginia.edu
Mingzhang YinAssistant Professor, University of FloridaCorreu electrònic verificat a ufl.edu
Maria De-ArteagaAssistant Professor, University of Texas at AustinCorreu electrònic verificat a utexas.edu
Matt LeaseProfessor, School of Information, University of Texas at AustinCorreu electrònic verificat a utexas.edu
Min Kyung LeeProfessor of Human-Computer Interaction, University of Texas at AustinCorreu electrònic verificat a austin.utexas.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityCorreu electrònic verificat a columbia.edu
Zhendong WangUniversity of Texas at AustinCorreu electrònic verificat a utexas.edu
Mingyuan ZhouAssociate Professor, University of Texas at AustinCorreu electrònic verificat a mccombs.utexas.edu
Himabindu LakkarajuAssistant Professor, Harvard UniversityCorreu electrònic verificat a seas.harvard.edu
Markus EttlIBMCorreu electrònic verificat a us.ibm.com
Junyu CaoMcCombs School of Business, The University of Texas at AustinCorreu electrònic verificat a mccombs.utexas.edu
Esmaeil KeyvanshokoohMays Business School, Texas A&M UniversityCorreu electrònic verificat a tamu.edu
Steven ShuganUniversity of FloridaCorreu electrònic verificat a warrington.ufl.edu

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Ruijiang Gao

University of Texas at Dallas

Correu electrònic verificat a utdallas.edu - Pàgina d'inici

Machine Learning Human-AI Systems Causal Inference Generative Models


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Human-AI collaboration with bandit feedback R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, M Lease arXiv preprint arXiv:2105.10614, 2021	56	2021
Proxedit: Improving tuning-free real image editing with proximal guidance L Han, S Wen, Q Chen, Z Zhang, K Song, M Ren, R Gao, A Stathopoulos, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024	37	2024
Unsupervised domain adaptation via calibrating uncertainties L Han, Y Zou, R Gao, L Wang, D Metaxas CVPR Workshops 9, 2019	34	2019
Dual projection generative adversarial networks for conditional image generation L Han, MR Min, A Stathopoulos, Y Tian, R Gao, A Kadav, DN Metaxas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021	25	2021
Cost-accuracy aware adaptive labeling for active learning R Gao, M Saar-Tsechansky Proceedings of the AAAI conference on artificial intelligence 34 (03), 2569-2576, 2020	25	2020
Improving negative-prompt inversion via proximal guidance L Han, S Wen, Q Chen, Z Zhang, K Song, M Ren, R Gao, Y Chen, D Liu, ... arXiv preprint arXiv:2306.05414 3, 2023	22	2023
Probabilistic conformal prediction using conditional random samples Z Wang, R Gao, M Yin, M Zhou, DM Blei arXiv preprint arXiv:2206.06584, 2022	22	2022
Robust conditional GAN from uncertainty-aware pairwise comparisons L Han, R Gao, M Kim, X Tao, B Liu, D Metaxas Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 10909 …, 2020	18	2020
Ae-stylegan: Improved training of style-based auto-encoders L Han, SH Musunuri, MR Min, R Gao, Y Tian, D Metaxas Proceedings of the IEEE/CVF winter conference on applications of computer …, 2022	16	2022
Enhancing counterfactual classification performance via self-training R Gao, M Biggs, W Sun, L Han Proceedings of the AAAI conference on artificial intelligence 36 (6), 6665-6673, 2022	10*	2022
Loss functions for discrete contextual pricing with observational data M Biggs, R Gao, W Sun arXiv preprint arXiv:2111.09933, 2021	10	2021
Learning complementary policies for human-ai teams R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, W Sun, MK Lee, ... arXiv preprint arXiv:2302.02944, 2023	8	2023
Confounding-robust policy improvement with human-ai teams R Gao, M Yin arXiv preprint arXiv:2310.08824, 2023	7	2023
On the impact of algorithmic recourse on social segregation R Gao, H Lakkaraju International Conference on Machine Learning, 10727-10743, 2023	6	2023
Improving tuning-free real image editing with proximal guidance L Han, S Wen, Q Chen, Z Zhang, K Song, M Ren, R Gao, A Stathopoulos, ... arXiv preprint arXiv:2306.05414, 2023	2	2023
Hr-bandit: Human-ai collaborated linear recourse bandit J Cao, R Gao, E Keyvanshokooh arXiv preprint arXiv:2410.14640, 2024	1	2024
SEL-BALD: Deep Bayesian Active Learning with Selective Labels R Gao, M Yin, M Saar-Tsechansky Advances in Neural Information Processing Systems 37, 40952-40975, 2025		2025
Adjusting regression models for conditional uncertainty calibration R Gao, M Yin, J Mcinerney, N Kallus Machine Learning, 1-24, 2024		2024
Advancing human-AI systems: on robustness, decision making, and beyond R Gao		2024
Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design M Yin, R Gao, W Lin, SM Shugan arXiv preprint arXiv:2310.12026, 2023		2023

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