We investigate the approximation of functions f on a bounded domain
Ω⊂ℝ^...
Labeled data are critical to modern machine learning applications, but
o...
Deep neural networks (DNNs) trained to minimize a loss term plus the sum...
Deep learning has been wildly successful in practice and most
state-of-t...
Weight decay is one of the most widely used forms of regularization in d...
We study the problem of estimating an unknown function from noisy data u...
We develop a variational framework to understand the properties of funct...
We develop a variational framework to understand the properties of the
f...
Construction of tight confidence regions and intervals is central to
sta...
We develop a general framework based on splines to understand the
interp...
Overparameterized models that interpolate training data often display
su...
We study concentration inequalities for the Kullback--Leibler (KL) diver...
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
We consider a generalization of low-rank matrix completion to the case w...
Low-rank matrix completion (LRMC) problems arise in a wide variety of
ap...
Consider a generic r-dimensional subspace of R^d, r<d, and
suppose that ...
This paper studies graphical model selection, i.e., the problem of estim...
This paper proposes a simple adaptive sensing and group testing algorith...
This paper provides lower bounds on the convergence rate of Derivative F...
This paper studies the sample complexity of searching over multiple
popu...
This paper examines the problem of ranking a collection of objects using...
Statistical dependencies among wavelet coefficients are commonly represe...
This paper investigates the problem of determining a binary-valued funct...