17 citations · 40 across the 11 of their papers we have counts for
6 papers · 1 filter
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
Ru Huang, Kai Chang, Huan He +2
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of…
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker +3
Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to…
NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals
Huan He, Ziyuan Tang, Shifan Zhao +2
This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm ha…
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Jiali Cheng, George Dasoulas, Huan He +2
Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world ap…
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Huan He, Shifan Zhao, Yuanzhe Xi +1
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realisti…
Domain Adaptation for Time Series Under Feature and Label Shifts
Huan He, Owen Queen, Teddy Koker +3
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents…