Articles

1. Mai, X., & Liao, Z. (2025). The breakdown of gaussian universality in classification ofhigh-dimensional linear factor mixtures. (Accepted) 2025 International Conferenceon Learning Representations (ICLR).
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2. Yang, W., Wang, Z., Mai, X., Ling, Z., Qiu, R. C., & Liao, Z. (2024). Inconsistency of ESPRITDoA estimation for large arrays and a correction via RMT. 2024 European Signal Processing Conference (EUSIPCO), 2722–2726.
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3. Mai, X., Avestimehr, S., Ortega, A., & Soltanolkotabi, M. (2022). On the effectiveness of active learning by uncertainty sampling in classification of Gaussian mixture data. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4238–4242.
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4. Mai, X., & Couillet, R. (2021). Consistent semi-supervised graph regularization for high dimensional data. The Journal of Machine Learning Research, 22(94), 1–48.
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5. Mai, X., & Liao, Z. (2020). High dimensional classification via regularized and unregularized empirical risk minimization: precise error and optimal loss. ArXiv, 1905.13742.
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6. Mai, X., & Couillet, R. (2019). Revisiting and improving semi-supervised learning: a large dimensional approach. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3547–3551.
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7. Mai, X., Liao, Z., & Couillet, R. (2019). A large scale analysis of logistic regression: Asymptotic performance and new insights. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3357–3361.
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8. Mai, X., & Couillet, R. (2018). A random matrix analysis and improvement of semi-supervised learning for large dimensional data. The Journal of Machine Learning Research, 19(1), 3074–3100.
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9. Couillet, R., Liao, Z., & Mai, X. (2018). Classification asymptotics in the random matrix regime. 2018 26th European Signal Processing Conference (EUSIPCO), 1875–1879.
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10. Mai, X., & Couillet, R. (2018). Semi-Supervised Spectral Clustering. 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2012–2016.
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11. Mai, X., & Couillet, R. (2017). The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2821–2825.
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Thesis

1. Mai, X. (2019). Methods of random matrices for large dimensional statistical learning. Université Paris-Saclay.
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