
Hessian Eigenspectra of More Realistic Nonlinear Models
Given an optimization problem, the Hessian matrix and its eigenspectrum ...
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Sparse sketches with small inversion bias
For a tall n× d matrix A and a random m× n sketching matrix S, the sketc...
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Kernel regression in high dimension: Refined analysis beyond double descent
In this paper, we provide a precise characterize of generalization prope...
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Sparse Quantized Spectral Clustering
Given a large data matrix, sparsifying, quantizing, and/or performing ot...
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Precise expressions for random projections: Lowrank approximation and randomized Newton
It is often desirable to reduce the dimensionality of a large dataset by...
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A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
This article characterizes the exact asymptotics of random Fourier featu...
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Towards Efficient Training for Neural Network Quantization
Quantization reduces computation costs of neural networks but suffers fr...
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AdaBits: Neural Network Quantization with Adaptive BitWidths
Deep neural networks with adaptive configurations have gained increasing...
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Innerproduct Kernels are Asymptotically Equivalent to Binary Discrete Kernels
This article investigates the eigenspectrum of the inner producttype ke...
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Complete Dictionary Learning via ℓ^4Norm Maximization over the Orthogonal Group
This paper considers the fundamental problem of learning a complete (ort...
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High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality
In this article, we investigate a family of classification algorithms de...
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Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
This paper focuses on learning transferable adversarial examples specifi...
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A Geometric Approach of Gradient Descent Algorithms in Neural Networks
In this article we present a geometric framework to analyze convergence ...
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The Dynamics of Learning: A Random Matrix Approach
Understanding the learning dynamics of neural networks is one of the key...
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On the Spectrum of Random Features Maps of High Dimensional Data
Random feature maps are ubiquitous in modern statistical machine learnin...
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A Large Dimensional Analysis of Least Squares Support Vector Machines
In this article, a large dimensional performance analysis of kernel leas...
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Random matrices meet machine learning: a large dimensional analysis of LSSVM
This article proposes a performance analysis of kernel least squares sup...
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Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
In this paper, we provide a novel construction of the linearsized spect...
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Zhenyu Liao
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