| Cross-Entropy Loss Functions: Theoretical Analysis and Applications A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23803-23828, 2023 | 77 | 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 | 36 | 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 | 36 | 2020 |
| H-Consistency Bounds for Surrogate Loss Minimizers P Awasthi, A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 1117-1174, 2022 | 24 | 2022 |
| Multi-Class -Consistency Bounds P Awasthi, A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 35, 782-795, 2022 | 21 | 2022 |
| A finer calibration analysis for adversarial robustness P Awasthi, A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2105.01550, 2021 | 21 | 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 | 15 | 2023 |
| Two-stage learning to defer with multiple experts A Mao, C Mohri, M Mohri, Y Zhong Advances in neural information processing systems 36, 2024 | 14 | 2024 |
| -Consistency Bounds for Pairwise Misranking Loss Surrogates A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23743-23802, 2023 | 13 | 2023 |
| Ranking with Abstention A Mao, M Mohri, Y Zhong ICML Workshop on the Many Facets of Preference-Based Learning, 2023 | 13 | 2023 |
| DC-programming for neural network optimizations P Awasthi, A Mao, M Mohri, Y Zhong Journal of Global Optimization, 1-17, 2024 | 12 | 2024 |
| Principled Approaches for Learning to Defer with Multiple Experts A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2310.14774, 2023 | 10 | 2023 |
| -Consistency Bounds: Characterization and Extensions A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Structured prediction with stronger consistency guarantees A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 46903-46937, 2023 | 9 | 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 | 8 | 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 | 8 | 2024 |
| -Consistency Guarantees for Regression A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19480, 2024 | | 2024 |
| Regression with Multi-Expert Deferral A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19494, 2024 | | 2024 |
| Top- Classification and Cardinality-Aware Prediction A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19625, 2024 | | 2024 |
| Differentially Private Domain Adaptation with Theoretical Guarantees R Bassily, C Cortes, A Mao, M Mohri arXiv preprint arXiv:2306.08838, 2023 | | 2023 |
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.確認したメール アドレス: google.com
