Trustworthy

LLM-PBE: Assessing Data Privacy in Large Language Models

A comprehensive privacy assessment of LLMs.

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

A comprehensive trustworthiness assessment of compressed LLMs.

Safe and Robust Watermark Injection with a Single OoD Image

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

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning

Tracking IP leakage in federated learning.

Revisiting Data-Free Knowledge Distillation with Poisoned Teachers

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

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts

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 …

MECTA: Memory-Economic Continual Test-Time Model Adaptation

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 …

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

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 …

Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning

Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without requiring raw data to be shared. One major challenge of FL comes from heterogeneity in users, which may have …

Precautionary Unfairness in Self-Supervised Contrastive Pre-training

Recently, self-supervised contrastive pre-training has become the de facto regime, that allows for efficient downstream fine-tuning. Meanwhile, its fairness issues are barely studied, though they have drawn great attention from the machine learning …