Anqi Mao (Google Scholar)

I am a Ph.D. Candidate in Mathematics at the Courant Institute of Mathematical Sciences, New York University, where I am very fortunate to be advised by Prof. Mehryar Mohri. My main research interests are machine learning theory and algorithms.

Publications

1. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention.
   In Twenty-seventh Conference on Artificial Intelligence and Statistics (AISTATS 2024). Valencia, Spain, 2024.
2. Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao, and Yutao Zhong.
   Learning to Reject with a Fixed Predictor: Application to Decontextualization.
   In Twelfth International Conference on Learning Representations (ICLR 2024). Vienna, Austria, 2024.
3. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms.
   In Proceedings of the 35th International Conference on Algorithmic Learning Theory (ALT 2024). San Diego, California, 2024.
4. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   Principled Approaches for Learning to Defer with Multiple Experts.
   In International Symposium on Artificial Intelligence and Mathematics (ISAIM 2024). Fort Lauderdale, Florida, 2024.
5. Anqi Mao, Christopher Mohri, Mehryar Mohri, and Yutao Zhong.
   Two-stage learning to defer with multiple experts.
   In Advances in Neural Information Processing Systems (NeurIPS 2023). New Orleans, Louisiana, 2023.
6. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   Structured prediction with stronger consistency guarantees.
   In Advances in Neural Information Processing Systems (NeurIPS 2023). New Orleans, Louisiana, 2023.
7. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   H-consistency bounds: characterization and extensions.
   In Advances in Neural Information Processing Systems (NeurIPS 2023). New Orleans, Louisiana, 2023.
8. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   Cross-entropy loss functions: Theoretical analysis and applications.
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023). Honolulu, Hawaii, 2023.
9. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
   H-consistency bounds for pairwise misranking loss surrogates.
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023). Honolulu, Hawaii, 2023.
10. Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    Ranking with Abstention.
    ICML 2023 Workshop on the Many Facets of Preference-Based Learning. Honolulu, Hawaii, 2023.
11. Raef Bassily, Corinna Cortes, Anqi Mao, and Mehryar Mohri.
    Differentially Private Domain Adaptation with Theoretical Guarantees.
    CoRR, abs/2306.08838, 2023.
12. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    DC-programming for neural network optimizations.
    Journal of Global Optimization (JOGO), 2023.
13. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    Theoretically grounded loss functions and algorithms for adversarial robustness.
    In Twenty-sixth Conference on Artificial Intelligence and Statistics (AISTATS 2023). Valencia, Spain, 2023.
14. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    Multi-class H-consistency bounds.
    In Advances in Neural Information Processing Systems (NeurIPS 2022). New Orleans, Louisiana, 2022.
15. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    H-consistency bounds for surrogate loss minimizers.
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, 2022.
    (Long Presentation)
16. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    A Finer Calibration Analysis for Adversarial Robustness.
    CoRR, abs/2105.01550, 2021.
17. Pranjal Awasthi, Natalie S. Frank, Anqi Mao, Mehryar Mohri, and Yutao Zhong.
    Calibration and consistency of adversarial surrogate losses.
    In Advances in Neural Information Processing Systems (NeurIPS 2021). Online, 2021.
    (Spotlight Presentation)
18. Yingzhou Li, Jianfeng Lu, and Anqi Mao.
    Variational training of neural network approximations of solution maps for physical models.
    Journal of Computational Physics, 2020.

• Advanced Machine Learning (Teaching Assistant and Grader, NYU Spring 2024)
• Foundations of Machine Learning (Teaching Assistant and Grader, NYU Fall 2023)
• Foundations of Machine Learning (Teaching Assistant and Grader, NYU Spring 2023)
• Foundations of Machine Learning (Teaching Assistant and Grader, NYU Spring 2022)