Interpolators are unstable. For example, the mininum ℓ_2 norm least
squa...
This paper investigates gradient descent for solving low-rank matrix
app...
Most existing works solving Room-to-Room VLN problem only utilize RGB im...
Overparametrization often helps improve the generalization performance. ...
Matrix factorization is a popular framework for modeling low-rank data
m...
Sparse regression has been a popular approach to perform variable select...
Vision-and-language navigation (VLN), a frontier study aiming to pave th...
This paper proposes a convex formulation for sparse multicategory linear...
The famous scientist Hermann von Helmholtz was born 200 years ago. Many
...
From an optimizer's perspective, achieving the global optimum for a gene...
Distributed data naturally arise in scenarios involving multiple sources...
This paper studies robust mean estimators for distributions with only fi...
This paper proposes the capped least squares regression with an adaptive...
The inclusion of domain (point) sources into a three dimensional boundar...
An estimation method of Radio Frequency fingerprint (RFF) based on the
p...
We propose a supervised principal component regression method for relati...
Reconfigurable intelligent surface (RIS)-assisted wireless communication...
Affective computing and cognitive theory are widely used in modern
human...
Spike-and-slab priors are popular Bayesian solutions for high-dimensiona...
In the modern content-based image retrieval systems, there is an increas...
Heart disease is one of the most common diseases causing morbidity and
m...
The recent research of facial expression recognition has made a lot of
p...
This paper investigates tradeoffs among optimization errors, statistical...
In neuroscience, functional brain connectivity describes the connectivit...
Convex clustering has gained popularity recently due to its desirable
pe...
We derive a Gaussian approximation result for the maximum of a sum of ra...
Visual Question Answering (VQA) faces two major challenges: how to bette...
This paper investigates the high-dimensional linear regression with high...
Classical multidimensional scaling is an important tool for dimension
re...
We offer a survey of selected recent results on covariance estimation fo...
We propose user-friendly covariance matrix estimators that are robust ag...
Multidimensional scaling is an important dimension reduction tool in
sta...
We propose robust sparse reduced rank regression and robust sparse princ...
This paper considers the cooperative device-to-device (D2D) systems with...
We prove a sharp Bernstein inequality for general-state-space and not
ne...
We establish the counterpart of Hoeffding's lemma for Markov dependent r...
Big data is transforming our world, revolutionizing operations and analy...
Large-scale multiple testing with correlated and heavy-tailed data arise...
We consider the problem of learning high-dimensional Gaussian graphical
...