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Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset …

A new method for safely and robustly injecting watermark after training without training data.

We propose a new risk to published generative models that finetuning on generated samples can exacerbate the privacy leakage.

We make local LLMs to engineer privacy-preserving prompts that are transferrable for cloud models.

Tracking IP leakage in federated learning.

We propose a new metric to efficiently evaluate the privacy risks from gradient inversion and provides new insights.

We uncover the security risk of data-free distillation from a poisoned teacher and propose the first countermeasure.

Increasing concerns have been raised on deep learning fairness in recent years. Existing fairness-aware machine learning methods mainly focus on the fairness of in-distribution data. However, in real-world applications, it is common to have …

Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient …

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection …