activity
20192023
most citedFedDAG: Federated DAG Structure Learning

13 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion

Zuopeng Yang, Tianshu Chu, Xin Lin +4

The recent success of text-to-image generation diffusion models has also revolutionized semantic image editing, enabling the manipulation of images based on query/target texts. Des…

stat.ML2022

On the Sparse DAG Structure Learning Based on Adaptive Lasso

Danru Xu, Erdun Gao, Wei Huang +3

Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidenti…

cs.LG2022★ 11 cited

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Erdun Gao, Ignavier Ng, Mingming Gong +5

State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as…

cs.LG2021★ 13 cited

FedDAG: Federated DAG Structure Learning

Erdun Gao, Junjia Chen, Li Shen +3

To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, d…

cs.MM2019★ 3 cited

Adaptive Embedding Pattern for Grayscale-Invariance Reversible Data Hiding

Erdun Gao, Zhibin Pan, Xinyi Gao

In traditional reversible data hiding (RDH) methods, researchers pay attention to enlarge the embedding capacity (EC) and to reduce the embedding distortion (ED). Recently, a compl…