Semi-supervised learning has substantially advanced medical image
segmen...
This paper presents a new optimization approach to causal estimation. Gi...
Representation learning constructs low-dimensional representations to
su...
Large-scale, two-sided matching platforms must find market outcomes that...
Medical imaging plays a pivotal role in diagnosis and treatment in clini...
Manually segmenting the hepatic vessels from Computer Tomography (CT) is...
While many areas of machine learning have benefited from the increasing
...
Though deep learning has achieved advanced performance recently, it rema...
Automatic brain tumor segmentation from multi-modality Magnetic Resonanc...
Sparse sequences of neural spikes are posited to underlie aspects of wor...
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreadi...
DNA code design aims to generate a set of DNA sequences (codewords) with...
Accurate segmentation of lung and infection in COVID-19 CT scans plays a...
Wang and Blei (2019) studies multiple causal inference and proposes the
...
Music tone quality evaluation is generally performed by experts. It coul...
There is a large body of literature linking anatomic and geometric
chara...
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multipl...
Automated segmentation of kidney and tumor from 3D CT scans is necessary...
Smartphones store a significant amount of personal and private informati...
We develop a robust data fusion algorithm for field reconstruction of
mu...
Unobserved confounding is a major hurdle for causal inference from
obser...
Matching methods are widely used for causal inference in observational
s...
Machine learning (ML) can automate decision-making by learning to predic...
Variational Bayes (VB) is a scalable alternative to Markov chain Monte C...
Causal estimation of treatment effect has an important role in guiding
p...
We consider causal inference in the presence of unobserved confounding. ...
The goal of a recommender system is to show its users items that they wi...
Analyzing large-scale, multi-experiment studies requires scientists to t...
Causal inference from observation data often assumes "strong ignorabilit...
Sure Independence Screening is a fast procedure for variable selection i...
A key challenge for modern Bayesian statistics is how to perform scalabl...
Probabilistic models analyze data by relying on a set of assumptions. Da...