Pan Li

Machine unlearning methods are vital for respecting data privacy rights.

Introducing a method to improve predictions in changing graph data environments.

Explore STochastic Gradient Langevin Unlearning for data privacy in machine learning.

A new model redefines graph analysis using point sets for improved predictions.

New method GSSC enhances graph analysis efficiency and effectiveness.

This work enhances privacy in graph diffusion while maintaining data utility.

This study introduces a new method for positional encodings in directed graphs.

New model simplifies the creation of directed acyclic graphs.

Researchers improve protein model training using diverse data and efficient methods.