Cross-entropy loss functions: Theoretical analysis and applications A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23803-23828, 2023 | 386 | 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 | 54 | 2021 |
H-consistency bounds for surrogate loss minimizers P Awasthi, A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 1117-1174, 2022 | 39 | 2022 |
Two-stage learning to defer with multiple experts A Mao, C Mohri, M Mohri, Y Zhong Advances in neural information processing systems 36, 2023 | 36 | 2023 |
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 | 36 | 2023 |
Multi-Class -Consistency Bounds P Awasthi, A Mao, M Mohri, Y Zhong Advances in neural information processing systems 35, 782-795, 2022 | 35 | 2022 |
A finer calibration analysis for adversarial robustness P Awasthi, A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2105.01550, 2021 | 33 | 2021 |
DC-programming for neural network optimizations P Awasthi, A Mao, M Mohri, Y Zhong Journal of Global Optimization, 1-17, 2024 | 28 | 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 | 25 | 2024 |
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 | 24 | 2024 |
Principled Approaches for Learning to Defer with Multiple Experts A Mao, M Mohri, Y Zhong International Symposium on Artificial Intelligence and Mathematics, 2024 | 23 | 2024 |
Ranking with Abstention A Mao, M Mohri, Y Zhong ICML Workshop on the Many Facets of Preference-Based Learning, 2023 | 21 | 2023 |
-Consistency Bounds for Pairwise Misranking Loss Surrogates A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 23743-23802, 2023 | 21 | 2023 |
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 | 19 | 2024 |
-Consistency Bounds: Characterization and Extensions A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 4470-4508, 2023 | 19 | 2023 |
Structured prediction with stronger consistency guarantees A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 36, 46903-46937, 2023 | 17 | 2023 |
Regression with Multi-Expert Deferral A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 34738-34759, 2024 | 10 | 2024 |
-Consistency Guarantees for Regression A Mao, M Mohri, Y Zhong International Conference on Machine Learning, 34712-34737, 2024 | 7 | 2024 |
Top- Classification and Cardinality-Aware Prediction A Mao, M Mohri, Y Zhong arXiv preprint arXiv:2403.19625, 2024 | 7 | 2024 |
A Universal Growth Rate for Learning with Smooth Surrogate Losses A Mao, M Mohri, Y Zhong Advances in Neural Information Processing Systems 37, 2024 | 6 | 2024 |