124 citations · 953 across the 47 of their papers we have counts for
18 papers · 1 filter
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Luofeng Liao, Zuyue Fu, Zhuoran Yang +3
In offline reinforcement learning (RL) an optimal policy is learned solely from a priori collected observational data. However, in observational data, actions are often confounded…
Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints
You-Lin Chen, Zhaoran Wang, Mladen Kolar
Training a classifier under non-convex constraints has gotten increasing attention in the machine learning community thanks to its wide range of applications such as algorithmic fa…
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion
Yi Chen, Jinglin Chen, Jing Dong +2
Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature…
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang +3
Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration…
Provable Gaussian Embedding with One Observation
Ming Yu, Zhuoran Yang, Tuo Zhao +2
The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined h…
High-dimensional Varying Index Coefficient Models via Stein's Identity
Sen Na, Zhuoran Yang, Zhaoran Wang +1
We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link…
A convex formulation for high-dimensional sparse sliced inverse regression
Kean Ming Tan, Zhaoran Wang, Tong Zhang +2
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of informatio…
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Chris Junchi Li, Zhaoran Wang, Han Liu
Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, esp…
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models
Yingxiang Yang, Adams Wei Yu, Zhaoran Wang +1
We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs). We sho…
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Zhuoran Yang, Lin F. Yang, Ethan X. Fang +3
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models. To address this issue, we ta…
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Kean Ming Tan, Zhaoran Wang, Han Liu +1
Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonica…
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
Zhaoran Wang, Quanquan Gu, Han Liu
We study the fundamental tradeoffs between computational tractability and statistical accuracy for a general family of hypothesis testing problems with combinatorial structures. Ba…
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
Zhuoran Yang, Zhaoran Wang, Han Liu +2
We study parameter estimation and asymptotic inference for sparse nonlinear regression. More specifically, we assume the data are given by , where is n…
Optimal linear estimation under unknown nonlinear transform
Xinyang Yi, Zhaoran Wang, Constantine Caramanis +1
Linear regression studies the problem of estimating a model parameter , from observations from linear model $y_i = \langl…
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang, Quanquan Gu, Yang Ning +1
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For…
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time
Zhaoran Wang, Huanran Lu, Han Liu
Sparse principal component analysis (PCA) involves nonconvex optimization for which the global solution is hard to obtain. To address this issue, one popular approach is convex rel…
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
Zhaoran Wang, Fang Han, Han Liu
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension to scale wit…
Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
Zhaoran Wang, Han Liu, Tong Zhang
We provide theoretical analysis of the statistical and computational properties of penalized -estimators that can be formulated as the solution to a possibly nonconvex optimizat…