228 citations · 392 across the 12 of their papers we have counts for
13 papers
MINTS: Minimalist Thompson Sampling
Kaizheng Wang
The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorpor…
Optimal Semiparametric Dynamic Pricing with Feature Diversity
Jinhang Chai, Yaqi Duan, Jianqing Fan +1
We study contextual dynamic pricing under a semiparametric demand model in which the purchase probability is , where captures mean utility as…
Localized exploration in contextual dynamic pricing achieves dimension-free regret
Jinhang Chai, Yaqi Duan, Jianqing Fan +1
We study the problem of contextual dynamic pricing with a linear demand model. We propose a novel localized exploration-then-commit (LetC) algorithm which starts with a pure explor…
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow
Yuling Yan, Kaizheng Wang, Philippe Rigollet
Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications. Unfortunately, fitting thes…
Adaptive and Robust Multi-Task Learning
Yaqi Duan, Kaizheng Wang
We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a f…
An theory of PCA and spectral clustering
Emmanuel Abbe, Jianqing Fan, Kaizheng Wang
Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning. While existing study of PCA focuses on the recovery of principal components and their asso…
Communication-Efficient Accurate Statistical Estimation
Jianqing Fan, Yongyi Guo, Kaizheng Wang
When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy conc…
Robust high dimensional factor models with applications to statistical machine learning
Jianqing Fan, Kaizheng Wang, Yiqiao Zhong +1
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomic…
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
Cong Ma, Kaizheng Wang, Yuejie Chi +1
Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems. Due to the highly nonconvex nature o…
Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank
Emmanuel Abbe, Jianqing Fan, Kaizheng Wang +1
Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking,…
Spectral Method and Regularized MLE Are Both Optimal for Top- Ranking
Yuxin Chen, Jianqing Fan, Cong Ma +1
This paper is concerned with the problem of top- ranking from pairwise comparisons. Given a collection of items and a few pairwise comparisons across them, one wishes to ide…
Distributed Estimation of Principal Eigenspaces
Jianqing Fan, Dong Wang, Kaizheng Wang +1
Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data…
Factor-Adjusted Regularized Model Selection
Jianqing Fan, Yuan Ke, Kaizheng Wang
This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency. Most commonly-used model select…