| Cross-Entropy Loss Functions: Theoretical Analysis and Applications A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23803-23828, 2023 | 139 | 2023 |
| Calibration and consistency of adversarial surrogate losses P Awasthi, N Frank, A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 34, 9804-9815, 2021 | 49 | 2021 |
| Variational training of neural network approximations of solution maps for physical models Y Li, J Lu, A Mao Journal of Computational Physics 409, 109338, 2020 | 38 | 2020 |
| H-Consistency Bounds for Surrogate Loss Minimizers P Awasthi, A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 1117-1174, 2022 | 35 | 2022 |
| Multi-Class -Consistency Bounds P Awasthi, A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 35, 782-795, 2022 | 30 | 2022 |
| A finer calibration analysis for adversarial robustness P Awasthi, A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2105.01550, 2021 | 29 | 2021 |
| Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness P Awasthi, A Mao, M Mohri, Y Zhong International Conference on Artificial Intelligence and Statistics, 10077-10094, 2023 | 26 | 2023 |
| Two-stage learning to defer with multiple experts A Mao, C Mohri, M Mohri, Y Zhong Advances in neural information processing systems 36, 2023 | 25 | 2023 |
| DC-programming for neural network optimizations P Awasthi, A Mao, M Mohri, Y Zhong Journal of Global Optimization, 1-17, 2024 | 23 | 2024 |
| Ranking with Abstention A Mao, M Mohri, Y Zhong ICML Workshop on the Many Facets of Preference-Based Learning, 2023 | 20 | 2023 |
| -Consistency Bounds for Pairwise Misranking Loss Surrogates A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23743-23802, 2023 | 20 | 2023 |
| Theoretically grounded loss functions and algorithms for score-based multi-class abstention A Mao, M Mohri, Y Zhong International Conference on Artificial Intelligence and Statistics, 4753-4761, 2024 | 18 | 2024 |
| Predictor-rejector multi-class abstention: Theoretical analysis and algorithms A Mao, M Mohri, Y Zhong International Conference on Algorithmic Learning Theory, 822-867, 2024 | 18 | 2024 |
| Learning to reject with a fixed predictor: Application to decontextualization C Mohri, D Andor, E Choi, M Collins, A Mao, Y Zhong International Conference on Learning Representations, 2024 | 17 | 2024 |
| Principled Approaches for Learning to Defer with Multiple Experts A Mao, M Mohri, Y Zhong International Symposium on Artificial Intelligence and Mathematics, 2024 | 16 | 2024 |
| Structured prediction with stronger consistency guarantees A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 46903-46937, 2023 | 16 | 2023 |
| -Consistency Bounds: Characterization and Extensions A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 4470-4508, 2023 | 16 | 2023 |
| -Consistency Guarantees for Regression A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 34712-34737, 2024 | 6 | 2024 |
| Regression with Multi-Expert Deferral A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 34738-34759, 2024 | 6 | 2024 |
| A Universal Growth Rate for Learning with Smooth Surrogate Losses A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2405.05968, 2024 | 6 | 2024 |
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.Verified email at google.com
