We study treatment effect estimation with functional treatments where th...
Tensor regression methods have been widely used to predict a scalar resp...
We study the implicit regularization of gradient descent towards structu...
The prevalence of data collected on the same set of samples from multipl...
Offline policy evaluation (OPE) is considered a fundamental and challeng...
In this paper, we study the implicit bias of gradient descent for sparse...
In this paper, we propose a novel method for matrix completion under gen...
We study nonparametric estimation for the partially conditional average
...
Tensor linear regression is an important and useful tool for analyzing t...
Measuring and testing the dependency between multiple random functions i...
We propose a novel broadcasting idea to model the nonlinearity in tensor...
Multidimensional function data arise from many fields nowadays. The
cova...
In this paper, we consider matrix completion with absolute deviation los...
A remarkable recent discovery in machine learning has been that deep neu...
This paper investigates the problem of adjusting for spatial effects in
...
This paper investigates the problem of matrix completion from corrupted ...
Estimating the probabilities of linkages in a network has gained increas...
Recent progress in learning theory has led to the emergence of provable
...
Sparse coding is a crucial subroutine in algorithms for various signal
p...
This paper considers the problem of matrix completion when the observed
...