Search Results for author:
Found 8 papers, 6 papers with code
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation.
Medical Federated Model with Mixture of Personalized and Sharing Components
Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Causal discovery from time-series data has been a central task in machine learning.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
Real order total variation with applications to the loss functions in learning schemes
Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings.
Deep representation learning for individualized treatment effect estimation using electronic health records
We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation.
Ranked #3 on
Causal Inference
on IHDP
