| A Random Matrix Approach to Neural Networks C Louart, Z Liao, R Couillet The Annals of Applied Probability 28 (2), 1190-1248, 2018 | 208 | 2018 |
| A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, A Precise Phase Transition, and the Corresponding Double Descent Z Liao, R Couillet, MW Mahoney Advances in Neural Information Processing Systems (NeurIPS), 124006, 2020 | 119 | 2020 |
| Random matrix methods for machine learning R Couillet, Z Liao Cambridge University Press, 2022 | 98 | 2022 |
| A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights X Mai, Z Liao, R Couillet 2019 IEEE International Conference on Acoustics, Speech and Signal …, 2019 | 68 | 2019 |
| On the Spectrum of Random Features Maps of High Dimensional Data Z Liao, R Couillet International Conference on Machine Learning (ICML) 80, 3063-3071, 2018 | 67 | 2018 |
| A Large Dimensional Analysis of Least Squares Support Vector Machines Z Liao, R Couillet IEEE Transactions on Signal Processing 67 (4), 1065-1074, 2019 | 61 | 2019 |
| Kernel Regression in High Dimensions: Refined Analysis Beyond Double Descent F Liu, Z Liao, JAK Suykens International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 | 60 | 2021 |
| The Dynamics of Learning: A Random Matrix Approach Z Liao, R Couillet International Conference on Machine Learning (ICML) 80, 3072-3081, 2018 | 54 | 2018 |
| High Dimensional Classification via Regularized and Unregularized Empirical Risk Minimization: Precise Error and Optimal Loss X Mai, Z Liao arXiv preprint arXiv:1905.13742, 2019 | 39* | 2019 |
| Precise Expressions for Random Projections: Low-rank Approximation and Randomized Newton M Dereziński, F Liang, Z Liao, MW Mahoney Advances in Neural Information Processing Systems (NeurIPS), 2020 | 33 | 2020 |
| Hessian Eigenspectra of More Realistic Nonlinear Models Z Liao, MW Mahoney Advances in Neural Information Processing Systems (NeurIPS), 2021 | 31 | 2021 |
| Sparse Quantized Spectral Clustering Z Liao, R Couillet, MW Mahoney The Ninth International Conference on Learning Representations (ICLR), 2021 | 27 | 2021 |
| Sparse sketches with small inversion bias M Derezinski, Z Liao, E Dobriban, M Mahoney Conference on Learning Theory (COLT), 1467-1510, 2021 | 26 | 2021 |
| Classification Asymptotics in the Random Matrix Regime R Couillet, Z Liao, X Mai 26th European Signal Processing Conference (EUSIPCO), 1875-1879, 2018 | 21 | 2018 |
| A geometric approach of gradient descent algorithms in linear neural networks Y Chitour, Z Liao, R Couillet Mathematical Control and Related Fields 13 (3), 918-945, 2023 | 18* | 2023 |
| Random Matrices Meet Machine Learning: A Large Dimensional Analysis of LS-SVM Z Liao, R Couillet 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 12 | 2017 |
| On Inner-product Kernels of High Dimensional Data Z Liao, R Couillet 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 10 | 2019 |
| Random matrices in service of ML footprint: ternary random features with no performance loss HT Ali, Z Liao, R Couillet International Conference on Learning Representations (ICLR), 2022 | 9 | 2022 |
| "Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach L Gu, Y Du, Y Zhang, D Xie, S Pu, RC Qiu, Z Liao Advances in Neural Information Processing Systems (NeurIPS), 2022 | 7 | 2022 |
| Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures Z Ling, L Li, Z Feng, Y Zhang, F Zhou, RC Qiu, Z Liao arXiv preprint arXiv:2402.02697, 2024 | 5 | 2024 |
Romain COUILLETUGA IDEX DataScience Chair, GIPSA-lab, University Grenoble-AlpesVerified email at gipsa-lab.grenoble-inp.fr