Sledovat
Anqi Mao
Anqi Mao
PhD Student, Courant Institute of Mathematical Sciences
E-mailová adresa ověřena na: cims.nyu.edu - Domovská stránka
Název
Citace
Citace
Rok
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
A Mao, M Mohri, Y Zhong
International Conference on Machine Learning, 23803-23828, 2023
1212023
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
482021
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
382020
H-Consistency Bounds for Surrogate Loss Minimizers
P Awasthi, A Mao, M Mohri, Y Zhong
International Conference on Machine Learning, 1117-1174, 2022
302022
A finer calibration analysis for adversarial robustness
P Awasthi, A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2105.01550, 2021
282021
Multi-Class -Consistency Bounds
P Awasthi, A Mao, M Mohri, Y Zhong
Advances in Neural Information Processing Systems 35, 782-795, 2022
252022
Two-stage learning to defer with multiple experts
A Mao, C Mohri, M Mohri, Y Zhong
Advances in neural information processing systems 36, 2024
212024
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
212023
DC-programming for neural network optimizations
P Awasthi, A Mao, M Mohri, Y Zhong
Journal of Global Optimization, 1-17, 2024
172024
-Consistency Bounds for Pairwise Misranking Loss Surrogates
A Mao, M Mohri, Y Zhong
International Conference on Machine Learning, 23743-23802, 2023
162023
Ranking with Abstention
A Mao, M Mohri, Y Zhong
ICML Workshop on the Many Facets of Preference-Based Learning, 2023
162023
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
132024
Principled Approaches for Learning to Defer with Multiple Experts
A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2310.14774, 2023
132023
Predictor-rejector multi-class abstention: Theoretical analysis and algorithms
A Mao, M Mohri, Y Zhong
International Conference on Algorithmic Learning Theory, 822-867, 2024
122024
-Consistency Bounds: Characterization and Extensions
A Mao, M Mohri, Y Zhong
Advances in Neural Information Processing Systems 36, 2024
122024
Structured prediction with stronger consistency guarantees
A Mao, M Mohri, Y Zhong
Advances in Neural Information Processing Systems 36, 46903-46937, 2023
122023
Top- Classification and Cardinality-Aware Prediction
A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2403.19625, 2024
32024
A Universal Growth Rate for Learning with Smooth Surrogate Losses
A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2405.05968, 2024
22024
-Consistency Guarantees for Regression
A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2403.19480, 2024
22024
Regression with Multi-Expert Deferral
A Mao, M Mohri, Y Zhong
arXiv preprint arXiv:2403.19494, 2024
22024
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Články 1–20