5 papers
CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks
Siyuan Li, Zehao Liu, Xi Lin +6
As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolvi…
LEGATO: Good Identity Unlearning Is Continuous
Qiang Chen, Chun-Wun Cheng, Xiu Su +5
Machine unlearning has become a crucial role in enabling generative models trained on large datasets to remove sensitive, private, or copyright-protected data. However, existing ma…
Graph Unlearning Meets Influence-aware Negative Preference Optimization
Qiang Chen, Zhongze Wu, Ang He +6
Recent advancements in graph unlearning models have enhanced model utility by preserving the node representation essentially invariant, while using gradient ascent on the forget se…
DeCoP: Enhancing Self-Supervised Time Series Representation with Dependency Controlled Pre-training
Yuemin Wu, Zhongze Wu, Xiu Su +6
Modeling dynamic temporal dependencies is a critical challenge in time series pre-training, which evolve due to distribution shifts and multi-scale patterns. This temporal variabil…
MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization
Yunlong Zhao, Xiaoheng Deng, Xiu Su +4
Dataset distillation (DD) entails creating a refined, compact distilled dataset from a large-scale dataset to facilitate efficient training. A significant challenge in DD is the de…