Publications

Conference papers

  1. Y. Liu, Z. Liao. “Dual-Attention Convolution Experts for Sparse Tensor Completion.” Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD), 450–467 (2027). [preprint]
  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). [preprint]
  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). [preprint]
  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] [preprint]
  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) [preprint]
  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). [preprint]
  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). [preprint]
  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] [pdf] [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] [pdf] [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] [pdf] [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. 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.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–18 (2026). [preprint]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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). [preprint]
  2. S. Li, T. Hou, Z. Liao, T. Gao. “Latent Iterative Refinement Flow: A Geometric Constrained Approach for Limited-Data Generation.” (2026). [preprint]
  3. Z. Liao, J. Liu, T. Hou, D. Zou, Z. Ling. “On the Interpolation Error of Nonlinear Attention versus Linear Regression.” (2025). [preprint]
  4. Z. Liao, Y. Xia, C. Niu, Y. Xiao. “Analysis and Approximate Inference of Large Random Kronecker Graphs.” (2024). [preprint]
  5. 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]
  6. 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]
  7. 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). [preprint]
  8. Z. Liao, R. Couillet. “Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels.” (2019). [preprint]

Ph.D. thesis

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