17 citations · 40 across the 11 of their papers we have counts for
12 papers
Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation
Chin-Hung Huang, JooChul Lee, Huan He
Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is expensive, and closely related tri…
Anderson Acceleration with Truncated Gram-Schmidt
Ziyuan Tang, Tianshi Xu, Huan He +2
Anderson Acceleration (AA) is a popular algorithm designed to enhance the convergence of fixed-point iterations. In this paper, we introduce a variant of AA based on a Truncated Gr…
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…
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Huan He, Shifan Zhao, Ziyuan Tang +3
Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations. However, many acceleration methods require storing a large number of previous iterates an…
Domain generalization Person Re-identification on Attention-aware multi-operation strategery
Yingchun Guo, Huan He, Ye Zhu +1
Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which i…
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
Difeng Cai, Yuliang Ji, Huan He +2
Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing lite…
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration
Huan He, Shifan Zhao, Yuanzhe Xi +2
Many modern machine learning algorithms such as generative adversarial networks (GANs) and adversarial training can be formulated as minimax optimization. Gradient descent ascent (…