Skip to main navigation
Skip to search
Skip to main content
Illinois Experts Home
LOGIN & Help
Link opens in a new tab
Search content at Illinois Experts
Home
Profiles
Research units
Research & Scholarship
Datasets
Honors
Press/Media
Activities
PM2.5 forecasting under distribution shift: A graph learning approach
Yachuan Liu
,
Jiaqi Ma
, Paramveer Dhillon
, Qiaozhu Mei
School of Information Sciences
Siebel School of Computing and Data Science
National Center for Supercomputing Applications (NCSA)
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'PM2.5 forecasting under distribution shift: A graph learning approach'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Learning Approaches
100%
Distribution Shift
100%
Graph Learning
100%
PM2.5 Prediction
100%
Graph-based
40%
New Benchmark
40%
Graph Neural Network
40%
Benchmark Tasks
40%
Spatial-temporal Graph Neural Network
40%
Graph-based Machine Learning
40%
Neural Network Model
20%
Environmental Sensors
20%
Large Families
20%
Future Air Quality
20%
Graphical Models
20%
Distributed Network
20%
PM2.5 Concentration
20%
Geographically Distributed
20%
Spatio-temporal Prediction
20%
Machine Learning Models
20%
Prediction Task
20%
Split Method
20%
Learning Distributions
20%
Spatio-temporal Learning
20%
Computer Science
Learning Approach
100%
Graph Neural Network
100%
Benchmark Task
50%
Spatiotemporal Graph
50%
Machine Learning
50%
Learning System
50%
Neural Network Model
25%
Technical Challenge
25%
Distributed Network
25%
Special Attention
25%
Psychology
Neural Network
100%
Network Model
25%
Learning Model
25%