Completely randomized experiment is the gold standard for causal inferen...
Click-Through Rate (CTR) prediction is a fundamental technique in
recomm...
Automated radiology report generation aims to generate radiology reports...
Contrastive language-image Pre-training (CLIP) [13] can leverage large
d...
Large language models (LLMs) have already revolutionized code generation...
We consider a robust reinforcement learning problem, where a learning ag...
Treatment effect estimation under unconfoundedness is a fundamental task...
The division of goods in the online realm poses opportunities and challe...
Multi-task learning (MTL) aims at learning related tasks in a unified mo...
To facilitate research on text generation, this paper presents a
compreh...
We consider the problem of testing whether a single coefficient is equal...
The radiation dose in computed tomography (CT) examinations is harmful f...
In recent years, some researchers focused on using a single image to obt...
Automated radiology report generation aims at automatically generating a...
In this paper we consider the contextual multi-armed bandit problem for
...
We study the identification and estimation of long-term treatment effect...
Magnetic resonance imaging (MRI) is a widely used medical imaging modali...
A large number of coils are able to provide enhanced signal-to-noise rat...
Purpose: Although recent deep energy-based generative models (EBMs) have...
Completely randomized experiments have been the gold standard for drawin...
As an effective way to integrate the information contained in multiple
m...
This paper addresses robust beamforming design for rate splitting multip...
This work presents an unsupervised deep learning scheme that exploiting
...
Gaussian Bayesian networks (a.k.a. linear Gaussian structural equation
m...
Unsupervised deep learning has recently demonstrated the promise to prod...
We study identifiability of Andersson-Madigan-Perlman (AMP) chain graph
...
Deep neural networks are vulnerable to adversarial examples that are cra...
As 5G networks rolling out in many different countries nowadays, the tim...
This paper proposes an iterative generative model for solving the automa...
We consider estimation of average treatment effects given observational ...
Efficient exploration is one of the most important issues in deep
reinfo...
We consider the related problems of estimating the l_2-norm and the squa...
Inferring graph structure from observations on the nodes is an important...
Deep neural networks have achieved remarkable success in computer vision...
As systems are getting more autonomous with the development of artificia...
The rapidly growing parameter volume of deep neural networks (DNNs) hind...
Channel estimation is of crucial importance in massive multiple-input
mu...
Subcarrier assignment is of crucial importance in wideband cognitive rad...
Computation-efficient resource allocation strategies are of crucial
impo...
Ill-posed inverse problems in imaging remain an active research topic in...
We consider the problem of estimating causal DAG models from a mix of
ob...
In this paper the secure performance for the visible light communication...
Unmanned aerial vehicle (UAV)-enabled communication networks are promisi...
We consider the problem of estimating an undirected Gaussian graphical m...
We consider the problem of learning a causal graph in the presence of
me...
To achieve a good tradeoff between the consumed power and the harvested
...
We consider the problem of testing the hypothesis that the parameter of
...
We consider the problem of jointly estimating multiple related directed
...
We consider the problem of estimating the differences between two causal...
The explosive growth of mobile devices and the rapid increase of wideban...