290 citations · 476 across the 8 of their papers we have counts for
9 papers
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
Yuhao Zhou, Jintao Xu, Bingrui Li +3
Finding an -stationary point of a nonconvex function with a Lipschitz continuous Hessian is a central problem in optimization. Regularized Newton methods are a classical tool an…
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. A…
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they…
Fast Instrument Learning with Faster Rates
Ziyu Wang, Yuhao Zhou, Jun Zhu
We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments. We propose a simple algorithm which combines kernelized IV methods and an arbitra…
Gradient Estimation with Discrete Stein Operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang +2
Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems…
Quasi-Bayesian Dual Instrumental Variable Regression
Ziyu Wang, Yuhao Zhou, Tongzheng Ren +1
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quant…
A Semismooth Newton based Augmented Lagrangian Method for Nonsmooth Optimization on Matrix Manifolds
Yuhao Zhou, Chenglong Bao, Chao Ding +1
This paper is devoted to studying an augmented Lagrangian method for solving a class of manifold optimization problems, which have nonsmooth objective functions and nonlinear const…
Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu
Estimating the score, i.e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of prob…
ZhuSuan: A Library for Bayesian Deep Learning
Jiaxin Shi, Jianfei Chen, Jun Zhu +4
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep…