5 papers
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…
Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation
Wenhao Li, Xiu Su, Jingyi Wu +5
Large Vision-Language Models (LVLMs) have demonstrated remarkable advancements in numerous areas such as multimedia. However, hallucination issues significantly limit their credibi…
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…