Conferences:

Z. Ling, L. Li, Z. Feng, Y. Zhang, F. Zhou, R. C. Qiu, Z. Liao “Deep Equilibrium Models are Almost Equivalent to Notsodeep Explicit Models for Highdimensional Gaussian Mixtures”, The Fortyfirst International Conference on Machine Learning (ICML 2024), 2024. preprint

Y. Song, K. Wan, Z. Liao, H. Xu, G. Caire, S. Shamai, “An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures”, 2024 IEEE International Symposium on Information Theory (ISIT 2024), 2024.

Y. Wang, Z. Feng, Z. Liao, “FedRFAdapt: Robust and CommunicationEfficient Federated Domain Adaptation via Random Features”, Workshop on Timely and Private Machine Learning over Networks, 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024), 2024. slides

Y. Du, Z. Ling, R. C. Qiu, Z. Liao, “Highdimensional Learning Dynamics of Deep Neural Nets in the Neural Tangent Regime”, Highdimensional Learning Dynamics Workshop, The Fortieth International Conference on Machine Learning (ICML 2023), 2023.

Z. Ling, Z. Liao, R. C. Qiu, “On the Equivalence Between Implicit and Explicit Neural Networks: A Highdimensional Viewpoint”, Highdimensional Learning Dynamics Workshop, The Fortieth International Conference on Machine Learning (ICML 2023), 2023.

L. Gu, Y. Du, Y. Zhang, D. Xie, S. Pu, R. C. Qiu, Z. Liao, ““Lossless” Compression of Deep Neural Networks: A Highdimensional Neural Tangent Kernel Approach”, The 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022. preprint by fixing typos in Theorems 1 and 2 from the NeurIPS 2022 proceeding version.

H. Tiomoko, Z. Liao, R. Couillet, “Random matrices in service of ML footprint: ternary random features with no performance loss”, The Tenth International Conference on Learning Representations (ICLR 2022), 2022. preprint

Z. Liao, M. W. Mahoney, “Hessian Eigenspectra of More Realistic Nonlinear Models” (oral), The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021. preprint

M. Dereziński, Z. Liao, E. Dobriban, M. W. Mahoney, “Sparse sketches with small inversion bias”, The 34th Annual Conference on Learning Theory (COLT 2021), 2021. preprint

F. Liu, Z.Liao, J. A.K. Suykens, “Kernel regression in high dimension: Refined analysis beyond double descent”, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021. preprint

Z.Liao, R. Couillet, M. W. Mahoney, “Sparse Quantized Spectral Clustering” (spotlight), The Ninth International Conference on Learning Representations (ICLR 2021), 2021. poster, slides, and preprint

Z.Liao, R. Couillet, M. W. Mahoney, “A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, A Precise Phase Transition, and the Corresponding Double Descent”, The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020. poster and preprint

M. Dereziński, F. Liang, Z. Liao, M. W. Mahoney, “Precise expressions for random projections: Lowrank approximation and randomized Newton”, The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020. preprint

Z.Liao, R. Couillet, “On Innerproduct Kernels of High Dimensional Data”, IEEE International Workshop on Computational Advances in MultiSensor Adaptive Processing (CAMSAP 2019), Guadeloupe, French West Indies, 2019. preprint

X. Mai, Z. Liao, R. Couillet, “A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights”, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), Brighton, UK, 2019. poster and preprint

R. Couillet, Z. Liao, X. Mai, “Classification Asymptotics in the Random Matrix Regime”, The 26th European Signal Processing Conference (EUSIPCO 2018), Rome, Italy, 2018. preprint

Z. Liao, R. Couillet, “On the Spectrum of Random Features Maps of High Dimensional Data”, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018. (long talk) slides and preprint

Z. Liao, R. Couillet, “The Dynamics of Learning: A Random Matrix Approach”, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018. (long talk) slides and preprint

Z. Liao, R. Couillet, “Random Matrices Meet Machine Learning: A Large Dimensional Analysis of LSSVM”, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, USA, 2017. slides and preprint
Journals:

J. Wang, S. Zhang, J. Cai, Z. Liao, C. Arenz, R. Betzholz, “Robustness of randomcontrol quantumstate tomography”, Physical Review A 108 (2 Aug. 2023), 022408. preprint

Y. Chitour, Z. Liao, R. Couillet, “A geometric approach of gradient descent algorithms in linear neural networks”, Mathematical Control and Related Fields, 13(3) (2023), 918–945. preprint

Z.Liao, R. Couillet, M. W. Mahoney, “A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent”, Journal of Statistical Mechanics: Theory and Experiment 2021(12) (Dec. 2021), 124006. preprint

Z. Liao, R. Couillet, “A Large Dimensional Analysis of Least Squares Support Vector Machines”, IEEE Transactions on Signal Processing 67 (4) (Feb. 2019), 10651074. (University of ParisSaclay ED STIC Ph.D. Paper Award) preprint and supplementary material

C. Louart, Z. Liao, R. Couillet, “A Random Matrix Approach to Neural Networks”, The Annals of Applied Probability 28 (2) (Apr. 2018), 11901248. preprint
Preprints:

Z. Liao, Y. Xia, C. Niu, Y. Xiao “Analysis and Approximate Inference of Large Random Kronecker Graphs”, 2024.

W. Yang, Z. Wang, X. Mai, Z. Ling, R. C. Qiu, Z. Liao “Inconsistency of ESPRIT DoA Estimation for Large Arrays and a Correction via RMT”, 2024.

Y. Song, K. Wan, Z. Liao, G. Caire, “An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures”, 2023.

Z. Feng, Y. Wang, J. Li, F. Yang, J. Lou, T. Mi, R. C. Qiu, Z. Liao, “Robust and CommunicationEfficient Federated Domain Adaptation via Random Features”, 2023.

Z. Liao, R. Couillet, “Innerproduct Kernels are Asymptotically Equivalent to Binary Discrete Kernels”, 2019.

X. Mai, Z. Liao, “High Dimensional Classification via Regularized and Unregularized Empirical Risk Minimization: Precise Error and Optimal Loss”, 2019.
Ph.D. thesis:
Z. Liao, “A random matrix framework for large dimensional machine learning and neural networks”, CentraleSupélec, University ParisSaclay, September 2019. [slides]