Ensemble Gaussian Processes for Online Learning Over Graphs With Adaptivity and Scalability

Keyphrases

• Online Learning 100%
• Learning on Graphs 100%
• Adaptivity 100%
• Gaussian Process 100%
• One-hop 66%
• Uncertainty Quantification 33%
• Data-adaptive 33%
• Graph-based Semi-supervised Learning 33%
• Point Estimate 33%
• Synthetic Data 33%
• Semi-supervised Method 33%
• Learning Styles 33%
• Feature-based 33%
• Semi-supervised Learning 33%
• Network Science 33%
• Preserving Privacy 33%
• Regret Analysis 33%
• Desired Value 33%
• Adversarial Setting 33%
• Delay-sensitive 33%
• Supervised Learning Method 33%
• Alternative Input 33%
• Random Features 33%
• Adaptive Fashion 33%
• Incremental Learning 33%
• Kernel Approximation 33%
• Safety-critical Domain 33%
• Node Connectivity 33%
• Egonet Features 33%
• Science Application 33%
• New Nodes 33%
• Nodal Observation 33%
• Batch Data 33%

Computer Science

• Semisupervised Learning 100%
• Electronic Learning 100%
• Approximation (Algorithm) 33%
• Adversarial Setting 33%
• Synthetic Datasets 33%
• Measure Analysis 33%
• Network Science 33%
• Connectivity Node 33%
• Encrypted Version 33%
• Learning Approach 33%
• Point Estimate 33%