Zhenyu Liao - RankMe

50 papers A* 12A 1B 1Misc 2Journal 30Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diving into Kronecker Adapters: Component Design Matters. CoRR Jiayu Bai, Danchen Yu, Zhenyu Liao, Tianqi Hou, Feng Zhou, Robert C. Qiu, Zenan Ling
2026	J	`jnl`	Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models [Special Issue on the Mathematics of Deep Learning]. IEEE Signal Process. Mag. Zhenyu Liao, Michael W. Mahoney
2026	J	`jnl`	TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search. CoRR Jiuqi Wei, Zhenyu Liao, Ruoyu Han, Quanqing Xu, Chuanhui Yang, Themis Palpanas
2025	J	`jnl`	A Random Matrix Perspective of Echo State Networks: From Precise Bias-Variance Characterization to Optimal Regularization. CoRR Yessin Moakher, Malik Tiomoko, Cosme Louart, Zhenyu Liao
2025	A*	`conf`	Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. ICML Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney
2025	J	`jnl`	Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. CoRR Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney
2025	J	`jnl`	Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks. CoRR Yessin Moakher, Malik Tiomoko, Cosme Louart, Zhenyu Liao
2025	J	`jnl`	Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models. CoRR Zhenyu Liao, Michael W. Mahoney
2025	J	`jnl`	Robust and Communication-Efficient Federated Domain Adaptation via Random Features. IEEE Trans. Knowl. Data Eng. Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert Caiming Qiu, Zhenyu Liao
2025	J	`jnl`	Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search. Proc. ACM Manag. Data Jiuqi Wei, Xiaodong Lee, Zhenyu Liao, Themis Palpanas, Botao Peng
2025	A*	`conf`	The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures. ICLR Xiaoyi Mai, Zhenyu Liao
2024	J	`jnl`	"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach. CoRR Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, Shiliang Pu, Robert C. Qiu, Zhenyu Liao
2024	B	`conf`	An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures. ISIT Yi Song, Kai Wan, Zhenyu Liao, Hao Xu, Giuseppe Caire, Shlomo Shamai
2024	A*	`conf`	Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. ICML Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao
2024	J	`jnl`	Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. CoRR Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao
2024	—	`conf`	FedRF-Adapt: Robust and Communication-Efficient Federated Domain Adaptation via Random Features. ICASSP Workshops Yuanjie Wang, Zhanbo Feng, Zhenyu Liao
2024	—	`conf`	Inconsistency of ESPRIT DoA Estimation for Large Arrays and a Correction via RMT. EUSIPCO Wei Yang, Zhengyu Wang, Xiaoyi Mai, Zenan Ling, Robert Caiming Qiu, Zhenyu Liao
2024	J	`jnl`	Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search. CoRR Jiuqi Wei, Xiaodong Lee, Zhenyu Liao, Themis Palpanas, Botao Peng
2024	J	`jnl`	The Breakdown of Gaussian Universality in Classification of High-dimensional Mixtures. CoRR Xiaoyi Mai, Zhenyu Liao
2023	J	`jnl`	Analysis and Approximate Inference of Large and Dense Random Kronecker Graphs. CoRR Zhenyu Liao, Yuanqian Xia, Chengmei Niu, Yong Xiao
2023	J	`jnl`	On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint. CoRR Zenan Ling, Zhenyu Liao, Robert C. Qiu
2023	J	`jnl`	Robust and Communication-Efficient Federated Domain Adaptation via Random Features. CoRR Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert C. Qiu, Zhenyu Liao
2022	A*	`conf`	"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach. NeurIPS Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, Shiliang Pu, Robert C. Qiu, Zhenyu Liao
2022	A*	`conf`	Random matrices in service of ML footprint: ternary random features with no performance loss. ICLR Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet
2021	A*	`conf`	Hessian Eigenspectra of More Realistic Nonlinear Models. NeurIPS Zhenyu Liao, Michael W. Mahoney
2021	J	`jnl`	Hessian Eigenspectra of More Realistic Nonlinear Models. CoRR Zhenyu Liao, Michael W. Mahoney
2021	A	`conf`	Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2021	J	`jnl`	Random matrices in service of ML footprint: ternary random features with no performance loss. CoRR Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet
2021	A*	`conf`	Sparse Quantized Spectral Clustering. ICLR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2021	A*	`conf`	Sparse sketches with small inversion bias. COLT Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
2020	A*	`conf`	A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. NeurIPS Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. CoRR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	Kernel regression in high dimension: Refined analysis beyond double descent. CoRR Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2020	A*	`conf`	Precise expressions for random projections: Low-rank approximation and randomized Newton. NeurIPS Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
2020	J	`jnl`	Precise expressions for random projections: Low-rank approximation and randomized Newton. CoRR Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
2020	J	`jnl`	Sparse Quantized Spectral Clustering. CoRR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	Sparse sketches with small inversion bias. CoRR Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
2019	J	`jnl`	A Large Dimensional Analysis of Least Squares Support Vector Machines. IEEE Trans. Signal Process. Zhenyu Liao, Romain Couillet
2019	Misc	`conf`	A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights. ICASSP Xiaoyi Mai, Zhenyu Liao, Romain Couillet
2019	J	`jnl`	High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality. CoRR Xiaoyi Mai, Zhenyu Liao
2019	J	`jnl`	Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels. CoRR Zhenyu Liao, Romain Couillet
2019	—	`conf`	On Inner-Product Kernels of High Dimensional Data. CAMSAP Zhenyu Liao, Romain Couillet
2018	J	`jnl`	A Geometric Approach of Gradient Descent Algorithms in Neural Networks. CoRR Yacine Chitour, Zhenyu Liao, Romain Couillet
2018	—	`conf`	Classification Asymptotics in the Random Matrix Regime. EUSIPCO Romain Couillet, Zhenyu Liao, Xiaoyi Mai
2018	A*	`conf`	On the Spectrum of Random Features Maps of High Dimensional Data. ICML Zhenyu Liao, Romain Couillet
2018	J	`jnl`	On the Spectrum of Random Features Maps of High Dimensional Data. CoRR Zhenyu Liao, Romain Couillet
2018	A*	`conf`	The Dynamics of Learning: A Random Matrix Approach. ICML Zhenyu Liao, Romain Couillet
2018	J	`jnl`	The Dynamics of Learning: A Random Matrix Approach. CoRR Zhenyu Liao, Romain Couillet
2017	J	`jnl`	A Random Matrix Approach to Neural Networks. CoRR Cosme Louart, Zhenyu Liao, Romain Couillet
2017	Misc	`conf`	Random matrices meet machine learning: A large dimensional analysis of LS-SVM. ICASSP Zhenyu Liao, Romain Couillet