Publications

Conference papers

  1. Y. Liu, Z. Liao. “Dual-Attention Convolution Experts for Sparse Tensor Completion.” European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Research Track (ECML PKDD) (2026).
  2. C. Yaakoubi, C. Louart, M. Tiomoko, Z. Liao. “Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization.” Proceedings of the 43rd International Conference on Machine Learning (ICML) (2026).
  3. J. Bai, D. Yu, Z. Liao, T. Hou, F. Zhou, R. C. Qiu, Z. Ling. “Diving into Kronecker Adapters: Component Design Matters.” Proceedings of the 43rd International Conference on Machine Learning (ICML) (2026).
  4. Y. Xu, S. Zhang, G. Zhou, Z. Liao. “Optimal Large-scale Finite-horizon Optimization via Random Matrix Theory.” 2026 34th European Signal Processing Conference (EUSIPCO) (2026).
  5. C. Louart, Z. Liao, M. Tiomoko. “A Conditional-Gaussian View of Score Universality Through Low-Dimensional Statistics.” 2026 IEEE International Symposium on Information Theory Workshops (ISIT-W) (2026).
  6. Y. Xu, Z. Liao. “New Characterizations of Deep Neural Networks Beyond the Ultra-Wide Regime.” 2026 IEEE International Symposium on Information Theory Workshops (ISIT-W) (2026).
  7. Y. Xu, Z. Liao. “An Improved Convergence Analysis of Gossip Methods for Large Random Graphs.” ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22562-22566 (2026).
  8. Y. Moahker, M. Tiomoko, C. Louart, Z. Liao. “A Random Matrix Perspective of Echo State Networks: From Precise Bias-Variance Characterization to Optimal Regularization.” ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 20716-20720 (2026). [poster]
  9. J. Wei, Z. Liao, R. Han, Q. Xu, C. Yang, T. Palpanas. “TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search.” Proc. ACM Manag. Data (SIGMOD), 4(3) (2026). [preprint]
  10. C. Niu, Z. Liao, Z. Ling, M. W. Mahoney. “Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton.” Proceedings of the 42nd International Conference on Machine Learning (ICML), 267, 46649–46692 (2025). (Oral) [preprint]
  11. X. Mai, Z. Liao. “The Breakdown of Gaussian Universality in Classification of High-dimensional Mixtures.” International Conference on Learning Representations (ICLR) (2025). [preprint] [slides]
  12. J. Wei, X. Lee, Z. Liao, T. Palpanas, B. Peng. “Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search.” Proc. ACM Manag. Data (SIGMOD), 3(1) (2025). [preprint]
  13. 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 32nd European Signal Processing Conference (EUSIPCO), 2722–2726 (2024). (Best Student Paper Candidate) [here]
  14. Z. Ling, L. Li, Z. Feng, Y. Zhang, F. Zhou, R. C. Qiu, Z. Liao. “Deep Equilibrium Models Are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures.” Proceedings of the 41st International Conference on Machine Learning (ICML), 235, 30585–30609 (2024). [preprint]
  15. 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), 2460–2465 (2024).
  16. Y. Wang, Z. Feng, Z. Liao. “FedRF-Adapt: Robust and Communication-Efficient Federated Domain Adaptation via Random Features.” 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 615–619 (2024). [workshop on timely and private machine learning over networks]
  17. L. Gu, Y. Du, Y. Zhang, D. Xie, S. Pu, R. Qiu, Z. Liao. “"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach.” Advances in Neural Information Processing Systems (NeurIPS), 35, 3774–3787 (2022). (Spotlight) [preprint]
  18. H. T. Ali, Z. Liao, R. Couillet. “Random matrices in service of ML footprint: ternary random features with no performance loss.” International Conference on Learning Representations (ICLR) (2022). [preprint]
  19. Z. Liao, M. W. Mahoney. “Hessian Eigenspectra of More Realistic Nonlinear Models.” Advances in Neural Information Processing Systems (NeurIPS), 34, 20104–20117 (2021). (Oral) [preprint]
  20. M. Derezinski, Z. Liao, E. Dobriban, M. Mahoney. “Sparse sketches with small inversion bias.” Proceedings of Thirty Fourth Conference on Learning Theory (COLT), 134, 1467–1510 (2021). [preprint]
  21. Z. Liao, R. Couillet, M. W. Mahoney. “Sparse Quantized Spectral Clustering.” International Conference on Learning Representations (ICLR) (2021). (Spotlight) [poster] [slides] [preprint]
  22. F. Liu, Z. Liao, J. Suykens. “Kernel Regression in High Dimension: Refined Analysis beyond Double Descent.” Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 130, 649–657 (2021). [preprint]
  23. 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.” Advances in Neural Information Processing Systems (NeurIPS), 33, 13939–13950 (2020). [preprint]
  24. M. Derezinski, F. T. Liang, Z. Liao, M. W. Mahoney. “Precise expressions for random projections: Low-rank approximation and randomized Newton.” Advances in Neural Information Processing Systems (NeurIPS), 33, 18272–18283 (2020).
  25. Z. Liao, R. Couillet. “On Inner-Product Kernels of High Dimensional Data.” 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 579–583 (2019). [preprint]
  26. X. Mai, Z. Liao, R. Couillet. “A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights.” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3357–3361 (2019). [poster] [preprint]
  27. R. Couillet, Z. Liao, X. Mai. “Classification Asymptotics in the Random Matrix Regime.” The 26th European Signal Processing Conference (EUSIPCO), 1875–1879 (2018). [preprint]
  28. Z. Liao, R. Couillet. “The Dynamics of Learning: A Random Matrix Approach.” Proceedings of the 35th International Conference on Machine Learning (ICML), 80, 3072–3081 (2018). (Long Talk) [slides] [preprint]
  29. 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), 80, 3063–3071 (2018). (Long Talk) [slides] [preprint]
  30. Z. Liao, R. Couillet. “Random Matrices Meet Machine Learning: A Large Dimensional Analysis of LS-SVM.” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2397–2401 (2017). [slides] [preprint]

Journal papers

  1. Z. Liao, M. W. Mahoney. “Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models.” IEEE Signal Processing Magazine, 43(2), 93-106 (2026). [preprint]
  2. Z. Wang, W. Yang, X. Mai, Z. Ling, Z. Liao, R. C. Qiu. “A Large-Dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT.” IEEE Transactions on Signal Processing, 74, 2290–2303 (2026). [preprint]
  3. Z. Feng, Y. Wang, J. Li, F. Yang, J. Lou, T. Mi, R. C. Qiu, Z. Liao. “Robust and Communication-Efficient Federated Domain Adaptation via Random Features.” IEEE Transactions on Knowledge and Data Engineering, 37(3), 1411-1424 (2025). [preprint] [code]
  4. J. Wang, S. Zhang, J. Cai, Z. Liao, C. Arenz, R. Betzholz. “Robustness of random-control quantum-state tomography.” Phys. Rev. A, 108(2), 022408 (2023). [preprint]
  5. Y. Chitour, Z. Liao, R. Couillet. “A geometric approach of gradient descent algorithms in linear neural networks.” Mathematical Control and Related Fields, 13(3), 918-945 (2023). [preprint]
  6. 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), 124006 (2021). [preprint]
  7. Z. Liao, R. Couillet. “A Large Dimensional Analysis of Least Squares Support Vector Machines.” IEEE Transactions on Signal Processing, 67(4), 1065–1074 (2019). [preprint] [supplementary material]
  8. C. Louart, Z. Liao, R. Couillet. “A Random Matrix Approach to Neural Networks.” The Annals of Applied Probability, 28(2), 1190–1248 (2018). [preprint]

Preprints

  1. C. Niu, S. Garg, M. Derezinski, Z. Liao. “Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions.” (2026).
  2. S. Li, T. Hou, Z. Liao, T. Gao. “Latent Iterative Refinement Flow: A Geometric Constrained Approach for Limited-Data Generation.” (2026).
  3. Z. Liao, J. Liu, T. Hou, D. Zou, Z. Ling. “On the Interpolation Error of Nonlinear Attention versus Linear Regression.” (2025).
  4. J. Lin, Z. Ling, Z. Feng, J. Xu, M. Liao, F. Zhou, T. Hou, Z. Liao, R. C. Qiu. “IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks.” (2025).
  5. Z. Liao, Y. Xia, C. Niu, Y. Xiao. “Analysis and Approximate Inference of Large Random Kronecker Graphs.” (2024).
  6. Y. Du, Z. Ling, R. C. Qiu, Z. Liao. “High-dimensional Learning Dynamics of Deep Neural Nets in the Neural Tangent Regime.” High-dimensional Learning Dynamics Workshop, The Fortieth International Conference on Machine Learning (ICML) (2023). [high-dimensional learning dynamics workshop]
  7. Z. Ling, Z. Liao, R. C. Qiu. “On the Equivalence Between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint.” Proceedings of HiLD: High-dimensional Learning Dynamics Workshop, The Fortieth International Conference on Machine Learning (ICML) (2023). [high-dimensional learning dynamics workshop]
  8. X. Mai, Z. Liao. “High Dimensional Classification via Regularized and Unregularized Empirical Risk Minimization: Precise Error and Optimal Loss.” arXiv preprint arXiv:1905.13742 (2020).
  9. Z. Liao, R. Couillet. “Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels.” (2019).

Ph.D. thesis

  1. Z. Liao. “A random matrix framework for large dimensional machine learning and neural networks.” (2019). [slides]