This paper studies the binary classification of unbounded data from ℝ^d ...
Given a sequence of observable variables {(x_1, y_1), …, (x_n,
y_n)}, th...
In this paper, we derive a novel bound on the generalization error of
Ma...
We propose an adjusted Wasserstein distributionally robust estimator – b...
In statistics, the least absolute shrinkage and selection operator (Lass...
This paper improves the state-of-the-art rate of a first-order algorithm...
Online learning naturally arises in many statistical and machine learnin...
We observe that computing empirical Wasserstein distance in the independ...
It is frequently observed that overparameterized neural networks general...
Count data occur widely in many bio-surveillance and healthcare applicat...
We study the Stochastic Gradient Descent (SGD) algorithm in nonparametri...
In machine learning and statistical data analysis, we often run into
obj...
We propose a novel accelerated stochastic algorithm – primal-dual
accele...
In this paper, we propose a two-stage method called Spline Assisted Part...
We prove the support recovery for a general class of linear and nonlinea...
In optimization, it is known that when the objective functions are stric...
Given an inhomogeneous chain embedded in a noisy image, we consider the
...
In image detection, one problem is to test whether the set, though mostl...
We propose a combined model, which integrates the latent factor model an...
Shape control is critical to ensure the quality of composite fuselage
as...
This paper introduces a new way to calculate distance-based statistics,
...
This paper introduces a new method named Distance-based Independence
Scr...
Under the linear regression framework, we study the variable selection
p...
Distributed statistical inference has recently attracted enormous attent...