Machine learning is increasingly deployed in safety-critical domains whe...
We develop and analyze a principled approach to kernel ridge regression ...
Multispectral pedestrian detection is a technology designed to detect an...
Gaussian mixture models form a flexible and expressive parametric family...
We study a multi-factor block model for variable clustering and connect ...
Optimization equips engineers and scientists in a variety of fields with...
We study the problem of multi-task non-smooth optimization that arises
u...
We study the multi-task learning problem that aims to simultaneously ana...
We investigate a clustering problem with data from a mixture of Gaussian...
Principal Component Analysis (PCA) is a powerful tool in statistics and
...
This paper considers a canonical clustering problem where one receives
u...
This paper presents compact notations for concentration inequalities and...
When the data are stored in a distributed manner, direct application of
...
Factor models are a class of powerful statistical models that have been
...
Recent years have seen a flurry of activities in designing provably effi...
This paper is concerned with the problem of top-K ranking from pairwise
...