Deep Learning

PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics

PureGen purifies poisoned training data using iterative Langevin dynamics of Energy-Based Models and Denoising Diffusion Probabilistic Models, achieving state-of-the-art poison defense with minimal impact on classifier generalization.

Causal Structural Hypothesis Testing and Data Generation Models

CSHTEST and CSVHTEST use non-parametric structural causal knowledge and deep neural networks to perform hypothesis testing on causal models, validated on simulated DAGs, a synthetic pendulum dataset, and real-world medical trauma data.