Zhaoran Wang

I am a fifth-year graduate student in the Department of Operations Research and Financial Engineering at Princeton University, working with Professor Han Liu. My research is supported by the Microsoft Research PhD fellowship. Thanks, Microsoft!

The long-term goal of my research is to develop a new generation of statistical optimization methods, theory, and systems for large-scale data analytics and artificial intelligence. The specific research questions that I am trying to answer include:
  • How can we solve highly nonconvex statistical learning problems with provable statistical and computational guarantees? [Details]
  • How can we systematically trade off between statistical accuracy and various computational resources with Pareto efficiency? [Details]
With this focus in mind, my research interests span across statistics, optimization, machine learning, and information theory, as well as computer systems.

I co-organized the 2016 ICML workshop on advances in nonconvex analysis and optimization.

Papers [by Topic]

Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
Zhaoran Wang, Quanquan Gu, Han Liu
Under Revision at Annals of Statistics, 2016
[Arxiv]
Finding a Needle in Higher Moments: A Computational Minimax Theory for Tensor PCA
Zhaoran Wang*, Zhuoran Yang*, Junchi Li, Han Liu (*: equal contribution)
Submitted, 2016
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Jianqing Fan, Han Liu, Zhaoran Wang, Zhuoran Yang (alphabetical order)
Submitted, 2016
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Kean Ming Tan, Zhaoran Wang, Han Liu, Tong Zhang
Under Revision at Journal of the Royal Statistical Society: Series B, 2016
[Arxiv]
A Convex Formulation for High-Dimensional Sparse Sliced Inverse Regression
Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, Dennis Cook
Submitted, 2016
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Xinyang Yi*, Zhaoran Wang*, Zhuoran Yang*, Constantine Caramanis, Han Liu (*: equal contribution)
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[NIPS Version]
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Junchi Li, Zhaoran Wang, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[NIPS Version]
Agnostic Estimation for Misspecified Phase Retrieval Models
Matey Neykov, Zhaoran Wang, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[PDF] [NIPS Version]
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[Arxiv] [NIPS Version]
Blind Attacks on Machine Learners
Alex Beatson, Zhaoran Wang, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2016 (short version)
[NIPS Version]
Statistical Limits of Convex Relaxations
Zhaoran Wang, Quanquan Gu, Han Liu
International Conference on Machine Learning (ICML), 2016 (short version)
[Arxiv] [PDF] [ICML Version]
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference under Nonconvexity
Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang
International Conference on Machine Learning (ICML), 2016 (short version)
[Arxiv] [ICML Version]
Low-Rank and Sparse Structure Pursuit via Alternating Minimization
Quanquan Gu, Zhaoran Wang, Han Liu
International Conference on Artificial Intelligence & Statistics (AISTATS), 2016 (short version)
[AISTATS Version]
A Truth Discovery Approach with Theoretical Guarantee
Houping Xiao, Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su, Han Liu
ACM International Conference on Knowledge Discovery and Data Mining (KDD), 2016
[Link]
High-Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2015 (short version)
[Arxiv] [PDF] [NIPS Version]
Nonconvex Low-Rank Matrix Factorization via Inexact First-Order Oracle
Tuo Zhao, Zhaoran Wang, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2015 (short version)
Under Revision at Journal of Machine Learning Research, 2016
[PDF] [NIPS Version]
Optimal Linear Estimation under Unknown Nonlinear Transform
Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2015 (short version)
[Arxiv] [NIPS Version]
Sparse Tensor Graphical Model: Nonconvex Optimization and Statistical Inference
Wei Sun, Zhaoran Wang, Xiang Lyu, Han Liu, Guang Cheng
Advances in Neural Information Processing Systems (NIPS), 2015 (short version)
[Arxiv] [PDF] [NIPS Version]
Arrow: A Domain-Specific Language for Distributed Iterative Learning Algorithms
Zhixun Tan, Zhaoran Wang, Boxun Li, Yu Wang, Han Liu
Submitted, 2015
[GitHub]
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
Zhaoran Wang, Huanran Lu, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2014 (short version)
Under Revision at Journal of Machine Learning Research, 2016
[Arxiv] [NIPS Version]
Sparse PCA with Oracle Property
Quanquan Gu, Zhaoran Wang, Han Liu
Advances in Neural Information Processing Systems (NIPS), 2014 (short version)
[NIPS Version]
Optimal Computational and Statistical Rates of Convergence for Sparse Nonconvex Learning Problems
Zhaoran Wang, Han Liu, Tong Zhang
Annals of Statistics, 2014
[Arxiv] [Link]
INFORMS Data Mining Best Student Paper Finalist
A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming
Bingsheng He, Han Liu, Zhaoran Wang, Xiaoming Yuan (alphabetical order)
SIAM Journal on Optimization, 2014
[PDF] [Link]
ASA Best Student Paper in Statistical Learning & Data Mining
Sparse Principal Component Analysis for High-Dimensional Vector Autoregressive Models
Zhaoran Wang, Fang Han, Han Liu
International Conference on Artificial Intelligence & Statistics (AISTATS), 2013 (short version)
Under Revision at Machine Learning Journal, 2016
[Arxiv] [AISTATS Version]
AISTATS Notable Paper Award
Pub/Sub on Stream: A Novel Message Broker on Streaming Framework with QoS support
Zhaoran Wang, Yu Zhang, Xiaotao Chang, Xiang Mi, Yu Wang, Kun Wang, Huazhong Yang
ACM International Conference on Distributed Event-Based Systems (DEBS), 2012 (22% acceptance rate)
[Link]