| Cross-entropy loss functions: Theoretical analysis and applications A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23803-23828, 2023 | 124 | 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 | 48 | 2021 |
| H-consistency bounds for surrogate loss minimizers P Awasthi, A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 1117-1174, 2022 | 31 | 2022 |
| A finer calibration analysis for adversarial robustness P Awasthi, A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2105.01550, 2021 | 28 | 2021 |
| Multi-Class -Consistency Bounds P Awasthi, A Mao, M Mohri, Y Zhong Advances in neural information processing systems 35, 782-795, 2022 | 26 | 2022 |
| Two-stage learning to defer with multiple experts A Mao, C Mohri, M Mohri, Y Zhong Advances in neural information processing systems 36, 2024 | 22 | 2024 |
| 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 | 22 | 2023 |
| DC-programming for neural network optimizations P Awasthi, A Mao, M Mohri, Y Zhong Journal of Global Optimization, 1-17, 2024 | 18 | 2024 |
| -Consistency Bounds for Pairwise Misranking Loss Surrogates A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23743-23802, 2023 | 17 | 2023 |
| Ranking with Abstention A Mao, M Mohri, Y Zhong ICML Workshop on the Many Facets of Preference-Based Learning, 2023 | 17 | 2023 |
| Principled Approaches for Learning to Defer with Multiple Experts A Mao, M Mohri, Y Zhong International Symposium on Artificial Intelligence and Mathematics, 2023 | 14 | 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, 2023 | 14 | 2023 |
| -Consistency Bounds: Characterization and Extensions A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 4470-4508, 2024 | 13 | 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 | 13 | 2024 |
| Structured prediction with stronger consistency guarantees A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 46903-46937, 2023 | 13 | 2023 |
| Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms A Mao, M Mohri, Y Zhong International Conference on Algorithmic Learning Theory, 2023 | 13 | 2023 |
| A Universal Growth Rate for Learning with Smooth Surrogate Losses A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2405.05968, 2024 | 3 | 2024 |
| -Consistency Guarantees for Regression A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19480, 2024 | 3 | 2024 |
| Regression with Multi-Expert Deferral A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19494, 2024 | 3 | 2024 |
| Top- Classification and Cardinality-Aware Prediction A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19625, 2024 | 3 | 2024 |
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.在 google.com 的电子邮件经过验证
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.在 google.com 的电子邮件经过验证
pranjal awasthiGoogle在 rutgers.edu 的电子邮件经过验证