Zhenyu Liao

According to our database, Zhenyu Liao authored at least 28 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Breakdown of Gaussian Universality in Classification of High-dimensional Mixtures.

Xiaoyi Mai, Zhenyu Liao

CoRR, 2024

An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures.

Yi Song, Kai Wan, Zhenyu Liao, Hao Xu, Giuseppe Caire, Shlomo Shamai

Proceedings of the IEEE International Symposium on Information Theory, 2024

Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures.

Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao

Proceedings of the Forty-first International Conference on Machine Learning, 2024

FedRF-Adapt: Robust and Communication-Efficient Federated Domain Adaptation via Random Features.

Yuanjie Wang, Zhanbo Feng, Zhenyu Liao

Proceedings of the IEEE International Conference on Acoustics, 2024

Inconsistency of ESPRIT DoA Estimation for Large Arrays and a Correction via RMT.

Wei Yang, Zhengyu Wang, Xiaoyi Mai, Zenan Ling, Robert Caiming Qiu, Zhenyu Liao

Proceedings of the 32nd European Signal Processing Conference, 2024

2023

Robust and Communication-Efficient Federated Domain Adaptation via Random Features.

Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert C. Qiu, Zhenyu Liao

CoRR, 2023

On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint.

Zenan Ling, Zhenyu Liao, Robert C. Qiu

CoRR, 2023

Analysis and Approximate Inference of Large and Dense Random Kronecker Graphs.

Zhenyu Liao, Yuanqian Xia, Chengmei Niu, Yong Xiao

CoRR, 2023

2022

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach.

Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, Shiliang Pu, Robert C. Qiu, Zhenyu Liao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Random matrices in service of ML footprint: ternary random features with no performance loss.

Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Hessian Eigenspectra of More Realistic Nonlinear Models.

Zhenyu Liao, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sparse Quantized Spectral Clustering.

Zhenyu Liao, Romain Couillet, Michael W. Mahoney

Proceedings of the 9th International Conference on Learning Representations, 2021

Sparse sketches with small inversion bias.

Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney

Proceedings of the Conference on Learning Theory, 2021

Kernel regression in high dimensions: Refined analysis beyond double descent.

Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Kernel regression in high dimension: Refined analysis beyond double descent.

Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens

CoRR, 2020

A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent.

Zhenyu Liao, Romain Couillet, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Precise expressions for random projections: Low-rank approximation and randomized Newton.

Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

A Large Dimensional Analysis of Least Squares Support Vector Machines.

Zhenyu Liao, Romain Couillet

IEEE Trans. Signal Process., 2019

Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels.

Zhenyu Liao, Romain Couillet

CoRR, 2019

High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality.

Xiaoyi Mai, Zhenyu Liao

CoRR, 2019

A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights.

Xiaoyi Mai, Zhenyu Liao, Romain Couillet

Proceedings of the IEEE International Conference on Acoustics, 2019

On Inner-Product Kernels of High Dimensional Data.

Zhenyu Liao, Romain Couillet

Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019

2018

A Geometric Approach of Gradient Descent Algorithms in Neural Networks.

Yacine Chitour, Zhenyu Liao, Romain Couillet

CoRR, 2018

The Dynamics of Learning: A Random Matrix Approach.

Zhenyu Liao, Romain Couillet

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Spectrum of Random Features Maps of High Dimensional Data.

Zhenyu Liao, Romain Couillet

Proceedings of the 35th International Conference on Machine Learning, 2018

Classification Asymptotics in the Random Matrix Regime.

Romain Couillet, Zhenyu Liao, Xiaoyi Mai

Proceedings of the 26th European Signal Processing Conference, 2018

2017

A Random Matrix Approach to Neural Networks.

Cosme Louart, Zhenyu Liao, Romain Couillet

CoRR, 2017

Random matrices meet machine learning: A large dimensional analysis of LS-SVM.

Zhenyu Liao, Romain Couillet

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017