Search Results for author:
Found 14 papers, 2 papers with code
One-Way Matching of Datasets with Low Rank Signals
We study one-way matching of a pair of datasets with low rank signals.
Sparse GCA and Thresholded Gradient Descent
Based on a Lagrangian form of the sample optimization problem, we propose a thresholded gradient descent algorithm for estimating GCA loading vectors and matrices in high dimensions.
Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations
If the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$ are i. i. d.
Probability
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks
Focusing on the symmetric two block case, we establish minimax rates for both global estimation of the common structure and individualized estimation of layer-wise community structures.
Community detection in sparse latent space models
We show that a simple community detection algorithm originated from stochastic blockmodel literature achieves consistency, and even optimality, for a broad and flexible class of sparse latent space models.
Nonconvex Matrix Completion with Linearly Parameterized Factors
Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones.
Efficient random graph matching via degree profiles
This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges.
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing
This paper surveys some recent developments in fundamental limits and optimal algorithms for network analysis.
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
Latent space models are effective tools for statistical modeling and exploration of network data.
Community Detection in Degree-Corrected Block Models
Community detection is a central problem of network data analysis.
Optimal Estimation and Completion of Matrices with Biclustering Structures
Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (1972) where one seeks to cluster cases and variables simultaneously.
Kernel Additive Principal Components
We focus on smallest APCs to describe additive nonlinear constraints that are approximately satisfied by the data.
Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal $x \in \mathbb{R}^p$ from noisy quadratic measurements $y_j = (a_j' x )^2 + \epsilon_j$, $j=1, \ldots, m$, with independent sub-exponential noise $\epsilon_j$.
Achieving Optimal Misclassification Proportion in Stochastic Block Model
Community detection is a fundamental statistical problem in network data analysis.
