52 citations · 380 across the 35 of their papers we have counts for
12 papers · 1 filter
Dimension Independent Generalization Error by Stochastic Gradient Descent
Xi Chen, Qiang Liu, Xin T. Tong
One classical canon of statistics is that large models are prone to overfitting, and model selection procedures are necessary for high dimensional data. However, many overparameter…
Statistical Adaptive Stochastic Gradient Methods
Pengchuan Zhang, Hunter Lang, Qiang Liu +1
We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stoc…
Stein Variational Gradient Descent With Matrix-Valued Kernels
Dilin Wang, Ziyang Tang, Chandrajit Bajaj +1
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance…
Stein Variational Gradient Descent as Moment Matching
Qiang Liu, Dilin Wang
Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asympt…
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason D. Lee, Qiang Liu +1
Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the…
Stein Variational Gradient Descent Without Gradient
Jun Han, Qiang Liu
Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions. However, the standard SVGD requires calculating…