Search Results for author:
Found 24 papers, 7 papers with code
A Flexible Latent Space Model for Multilayer Networks
Entities often interact with each other through multiple types of relations, which are often represented as multilayer networks.
GSON: A Group-based Social Navigation Framework with Large Multimodal Model
As the number of service robots and autonomous vehicles in human-centered environments grows, their requirements go beyond simply navigating to a destination.
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world.
Minimax Regret Learning for Data with Heterogeneous Subgroups
Given the observed groups of data, we develop a min-max-regret (MMR) learning framework for general supervised learning, which targets to minimize the worst-group regret.
Fair Information Spread on Social Networks with Community Structure
While some IM algorithms aim to remedy disparity in information coverage using node attributes, none use the empirical com- munity structure within the network itself, which may be beneficial since communities directly affect the spread of information.
Conformal Prediction for Network-Assisted Regression
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics.
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
The latent block model (LBM) is a commonly used model-based tool for biclustering.
Latent space models for multiplex networks with shared structure
Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set.
Limiting laws and consistent estimation criteria for fixed and diverging number of spiked eigenvalues
Firstly, for fixed $p$, we propose a generalized estimation criterion that can consistently estimate, $k$, the number of spiked eigenvalues.
Statistics Theory Methodology Statistics Theory
Fast Network Community Detection with Profile-Pseudo Likelihood Methods
In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee.
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms.
Community models for networks observed through edge nominations
We propose a general model for a class of network sampling mechanisms based on recording edges via querying nodes, designed to improve community detection for network data collected in this fashion.
High-dimensional Gaussian graphical model for network-linked data
Graphical models are commonly used to represent conditional dependence relationships between variables.
A Flexible Generative Framework for Graph-based Semi-supervised Learning
In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure.
Online Multi-Object Tracking with Dual Matching Attention Networks
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets.
Ranked #5 on
Online Multi-Object Tracking
on MOT16
Covariance-Insured Screening
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size.
Link prediction for egocentrically sampled networks
Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes.
Generalized linear models with low rank effects for network data
Networks are a useful representation for data on connections between units of interests, but the observed connections are often noisy and/or include missing values.
Network cross-validation by edge sampling
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem.
Classification with Ultrahigh-Dimensional Features
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.
Estimating network edge probabilities by neighborhood smoothing
The estimation of probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising.
Community Detection in Networks with Node Features
Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice.
Detecting Overlapping Communities in Networks Using Spectral Methods
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice.
High-dimensional Mixed Graphical Models
While graphical models for continuous data (Gaussian graphical models) and discrete data (Ising models) have been extensively studied, there is little work on graphical models linking both continuous and discrete variables (mixed data), which are common in many scientific applications.
