| To trust or not to trust a classifier H Jiang, B Kim, M Guan, M Gupta Advances in neural information processing systems 31, 2018 | 550 | 2018 |
| Identifying and correcting label bias in machine learning H Jiang, O Nachum International conference on artificial intelligence and statistics, 702-712, 2020 | 377 | 2020 |
| Wasserstein fair classification R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa Uncertainty in artificial intelligence, 862-872, 2020 | 219 | 2020 |
| Scarf: Self-supervised contrastive learning using random feature corruption D Bahri, H Jiang, Y Tay, D Metzler arXiv preprint arXiv:2106.15147, 2021 | 184 | 2021 |
| Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals A Cotter, H Jiang, M Gupta, S Wang, T Narayan, S You, K Sridharan Journal of Machine Learning Research 20 (172), 1-59, 2019 | 184 | 2019 |
| Two-player games for efficient non-convex constrained optimization A Cotter, H Jiang, K Sridharan Algorithmic Learning Theory, 300-332, 2019 | 143 | 2019 |
| Training well-generalizing classifiers for fairness metrics and other data-dependent constraints A Cotter, M Gupta, H Jiang, N Srebro, K Sridharan, S Wang, B Woodworth, ... International Conference on Machine Learning, 1397-1405, 2019 | 121 | 2019 |
| Uniform convergence rates for kernel density estimation H Jiang International Conference on Machine Learning, 1694-1703, 2017 | 103 | 2017 |
| DBSCAN++: Towards fast and scalable density clustering J Jang, H Jiang International conference on machine learning, 3019-3029, 2019 | 98 | 2019 |
| Deep k-nn for noisy labels D Bahri, H Jiang, M Gupta International Conference on Machine Learning, 540-550, 2020 | 89 | 2020 |
| Stochastic bandits with linear constraints A Pacchiano, M Ghavamzadeh, P Bartlett, H Jiang International conference on artificial intelligence and statistics, 2827-2835, 2021 | 82 | 2021 |
| A general approach to fairness with optimal transport C Silvia, J Ray, S Tom, P Aldo, J Heinrich, A John Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3633-3640, 2020 | 82 | 2020 |
| Non-asymptotic uniform rates of consistency for k-nn regression H Jiang Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3999-4006, 2019 | 50 | 2019 |
| Nonparametric stochastic contextual bandits M Guan, H Jiang Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 43 | 2018 |
| Quickshift++: Provably good initializations for sample-based mean shift H Jiang, J Jang, S Kpotufe International Conference on Machine Learning, 2294-2303, 2018 | 41 | 2018 |
| Density Level Set Estimation on Manifolds with DBSCAN H Jiang International Conference on Machine Learning, 1684-1693, 2017 | 34 | 2017 |
| Modal-set estimation with an application to clustering H Jiang, S Kpotufe International Conference on Artificial Intelligence and Statistics, 1197-1206, 2017 | 29 | 2017 |
| Shape constraints for set functions A Cotter, M Gupta, H Jiang, E Louidor, J Muller, T Narayan, S Wang, ... International conference on machine learning, 1388-1396, 2019 | 25 | 2019 |
| MeanShift++: Extremely fast mode-seeking with applications to segmentation and object tracking J Jang, H Jiang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 23 | 2021 |
| Churn reduction via distillation H Jiang, H Narasimhan, D Bahri, A Cotter, A Rostamizadeh arXiv preprint arXiv:2106.02654, 2021 | 22 | 2021 |
