| Adversarial multiple source domain adaptation H Zhao, S Zhang, G Wu, JMF Moura, JP Costeira, GJ Gordon Advances in neural information processing systems, 8559-8570, 2018 | 850* | 2018 |
| On Learning Invariant Representations for Domain Adaptation H Zhao, R Tachet des Combes, K Zhang, GJ Gordon In Proceedings of the 36th International Conference on Machine Learning, 2019 | 826 | 2019 |
| A review of single-source deep unsupervised visual domain adaptation S Zhao, X Yue, S Zhang, B Li, H Zhao, B Wu, R Krishna, JE Gonzalez, ... IEEE Transactions on Neural Networks and Learning Systems 33 (2), 473-493, 2020 | 398 | 2020 |
| Inherent Tradeoffs in Learning Fair Representations H Zhao, GJ Gordon Journal of Machine Learning Research 23 (57), 1-26, 2022 | 326 | 2022 |
| Interpretable preferences via multi-objective reward modeling and mixture-of-experts H Wang, W Xiong, T Xie, H Zhao, T Zhang arXiv preprint arXiv:2406.12845, 2024 | 285 | 2024 |
| Self-Adaptive Hierarchical Sentence Model H Zhao, Z Lu, P Poupart In Proceedings of the Twenty-Fourth International Joint Conference on …, 2015 | 273 | 2015 |
| Rlhf workflow: From reward modeling to online rlhf H Dong, W Xiong, B Pang, H Wang, H Zhao, Y Zhou, N Jiang, D Sahoo, ... arXiv preprint arXiv:2405.07863, 2024 | 262 | 2024 |
| Domain adaptation with conditional distribution matching and generalized label shift R Tachet des Combes, H Zhao, YX Wang, GJ Gordon Advances in Neural Information Processing Systems 33, 19276-19289, 2020 | 258 | 2020 |
| Convolutional-Recurrent Neural Networks for Speech Enhancement H Zhao, S Zarar, I Tashev, CH Lee IEEE International Conference on Acoustics, Speech and Signal Processing …, 2018 | 246 | 2018 |
| On dyadic fairness: Exploring and mitigating bias in graph connections P Li, Y Wang, H Zhao, P Hong, H Liu International conference on learning representations, 2021 | 169 | 2021 |
| Invariant information bottleneck for domain generalization B Li, Y Shen, Y Wang, W Zhu, D Li, K Keutzer, H Zhao Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7399-7407, 2022 | 165 | 2022 |
| Conditional Learning of Fair Representations H Zhao, A Coston, T Adel, GJ Gordon International Conference on Learning Representations, 2020 | 153 | 2020 |
| Arithmetic control of llms for diverse user preferences: Directional preference alignment with multi-objective rewards H Wang, Y Lin, W Xiong, R Yang, S Diao, S Qiu, H Zhao, T Zhang arXiv preprint arXiv:2402.18571, 2024 | 137 | 2024 |
| Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation H Wang, H Zhao, B Li International conference on machine learning, 10991-11002, 2021 | 135 | 2021 |
| Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation B Li, Y Wang, S Zhang, D Li, T Darrell, K Keutzer, H Zhao arXiv preprint arXiv:2010.04647, 2020 | 131 | 2020 |
| On the Relationship between Sum-Product Networks and Bayesian Networks H Zhao, M Melibari, P Poupart In Proceedings of the 32nd International Conference on Machine Learning, 116 …, 2015 | 123 | 2015 |
| Mitigating the alignment tax of rlhf Y Lin, H Lin, W Xiong, S Diao, J Liu, J Zhang, R Pan, H Wang, W Hu, ... Proceedings of the 2024 Conference on Empirical Methods in Natural Language …, 2024 | 118* | 2024 |
| Continual Learning with Adaptive Weights (CLAW) T Adel, H Zhao, RE Turner International Conference on Learning Representations, 2020 | 110 | 2020 |
| Deep Fair Clustering for Visual Learning P Li, H Zhao, H Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 107 | 2020 |
| A Unified Approach for Learning the Parameters of Sum-Product Networks H Zhao, P Poupart, G Gordon Advances in Neural Information Processing Systems, 2016 | 91 | 2016 |
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
