Statistical network models are useful for understanding the underlying
f...
Information spread through social networks is ubiquitous. Influence maxi...
Polygenic hazard score (PHS) models designed for European ancestry (EUR)...
An important problem in network analysis is predicting a node attribute ...
Likelihood ratio tests and the Wilks theorems have been pivotal in stati...
Network data are often sampled with auxiliary information or collected
t...
Biclustering on bipartite graphs is an unsupervised learning task that
s...
Quantum amplitude estimation is a key sub-routine of a number of quantum...
Community detection for large networks is a challenging task due to the ...
Latent space models are frequently used for modeling single-layer networ...
In this paper, we study limiting laws and consistent estimation criteria...
The stochastic block model is one of the most studied network models for...
This paper introduces a general framework for survival analysis based on...
In this paper, we propose a flexible model for survival analysis using n...
Communities are a common and widely studied structure in networks, typic...
Network structure is growing popular for capturing the intrinsic relatio...
The time-varying effects model is a flexible and powerful tool for model...
Graphical models are commonly used to represent conditional dependence
r...
We consider a family of problems that are concerned about making predict...
In this paper, we propose an online Multi-Object Tracking (MOT) approach...
We propose a multi-step method, called Multi Screen Penalty (MSP), to
es...
Modern bio-technologies have produced a vast amount of high-throughput d...
Link prediction in networks is typically accomplished by estimating or
r...
Networks are a useful representation for data on connections between uni...
Although much progress has been made in classification with high-dimensi...
The estimation of probabilities of network edges from the observed adjac...
Many methods have been proposed for community detection in networks, but...
Community detection is a fundamental problem in network analysis which i...
While graphical models for continuous data (Gaussian graphical models) a...
Link prediction is one of the fundamental problems in network analysis. ...
There has been a lot of work fitting Ising models to multivariate binary...
We consider the generic regularized optimization problem
β̂(λ)=_βL(y,Xβ)...