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Found 22 papers, 4 papers with code
Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates
In this paper, we propose new estimation and inference procedures for nonparametric treatment effect functions with endogeneity and potentially high-dimensional covariates.
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error.
Adaptive Linear Estimating Equations
Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes.
Rate-Optimal Subspace Estimation on Random Graphs
We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph.
Tensor Principal Component Analysis in High Dimensional CP Models
It is designed to improve the alternating least squares estimator and other forms of the high order orthogonal iteration for tensors with low or moderately high CP ranks, and it is guaranteed to converge rapidly when the error of any given initial estimator is bounded by a small constant.
Asymptotic normality of robust $M$-estimators with convex penalty
This paper develops asymptotic normality results for individual coordinates of robust M-estimators with convex penalty in high-dimensions, where the dimension $p$ is at most of the same order as the sample size $n$, i. e, $p/n\le\gamma$ for some fixed constant $\gamma>0$.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Jaccard similarity is widely used as a distance measure in many machine learning and search applications.
De-Biasing The Lasso With Degrees-of-Freedom Adjustment
This modification takes the form of a degrees-of-freedom adjustment that accounts for the dimension of the model selected by Lasso.
Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries
To fill in this void, in this article, we characterize the fundamental statistical limits of noisy tensor completion by establishing minimax optimal rates of convergence for estimating a $k$th order low rank tensor under the general $\ell_p$ ($1\le p\le 2$) norm which suggest significant room for improvement over the existing approaches.
Theory of the GMM Kernel
We prove the theoretical limit of GMM and the consistency result, assuming that the data follow an elliptical distribution, which is a very general family of distributions and includes the multivariate $t$-distribution as a special case.
Incoherent Tensor Norms and Their Applications in Higher Order Tensor Completion
In this paper, we investigate the sample size requirement for a general class of nuclear norm minimization methods for higher order tensor completion.
Compressed Sensing with Very Sparse Gaussian Random Projections
We have developed two estimators: (i) the {\em tie estimator}, and (ii) the {\em absolute minimum estimator}.
On Tensor Completion via Nuclear Norm Minimization
To establish our results, we develop a series of algebraic and probabilistic techniques such as characterization of subdifferetial for tensor nuclear norm and concentration inequalities for tensor martingales, which may be of independent interests and could be useful in other tensor related problems.
Sparse Recovery with Very Sparse Compressed Counting
In this paper, we adopt very sparse Compressed Counting for nonnegative signal recovery.
Compressed Counting Meets Compressed Sensing
In particular, when p->0 the required number of measurements is essentially M=K\log N, where K is the number of nonzero coordinates of the signal.
Asymptotic normality and optimalities in estimation of large Gaussian graphical models
This paper considers a fundamental question: When is it possible to estimate low-dimensional parameters at parametric square-root rate in a large Gaussian graphical model?
Calibrated Elastic Regularization in Matrix Completion
This paper concerns the problem of matrix completion, which is to estimate a matrix from observations in a small subset of indices.
One Permutation Hashing
While minwise hashing is promising for large-scale learning in massive binary data, the preprocessing cost is prohibitive as it requires applying (e. g.,) $k=500$ permutations on the data.
Entropy Estimations Using Correlated Symmetric Stable Random Projections
Methods for efficiently estimating the Shannon entropy of data streams have important applications in learning, data mining, and network anomaly detections (e. g., the DDoS attacks).
Optimality of Graphlet Screening in High Dimensional Variable Selection
Compared to m-variate brute-forth screening that has a computational cost of p^m, the GS only has a computational cost of p (up to some multi-log(p) factors) in screening.
Sparse Matrix Inversion with Scaled Lasso
The penalty level of the scaled Lasso for each column is completely determined by data via convex minimization, without using cross-validation.
Nearly unbiased variable selection under minimax concave penalty
We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized variable selection in high-dimensional linear regression.
Statistics Theory Statistics Theory 62J05, 62J07 (Primary) 62H12, 62H25 (Secondary)
