Test-time adaptation (TTA) has been a widely-studied paradigm for adapting well-trained models to distributionally shifted test data. Recent works extend TTA to graphs using graph augmentations and self-supervised objectives in Euclidean space. However, graph-structured data often exhibit heterogeneity where semantic s...
Ling-Wei Wei, Dou Hu, Li Sun et al.· Proceedings of the 32nd ACM...· 0 citations
This tutorial provides a systematic overview of LLM personalization, covering core paradigms such as user-aware representations, prompt-based adaptation, memory augmentation, parameter-efficient fine-tuning, and continual learning.
Rui-Jie Wang, Qing-Kai Zeng, Xue-Fei Wang et al.· Proceedings of the 32nd ACM...· 0 citations
VERITAS is proposed, a poisoning-resilient locally private graph learning protocol built on a trust-but-verify paradigm that effectively defends against data poisoning attacks and significantly improves downstream graph learning utility under rigorous privacy guarantees.
It is shown that this felt penalty becomes behaviorally binding through SPARC, a byte-clean code-gated reflection mechanism: LLM merchants fabricate when lying is free but restrain themselves when fabrication costs them sales, a self-interested response rather than compliance.
This survey presents the first comprehensive and systematic review of Differentially Private Graph Learning (DPGL), and organizes existing DPGL methods into four categories based on the granularity of privacy protection, namely node-level, edge-level, graph-level, and node-level.
Li Sun, Long-Zhu He, Ming Li et al.· Transactions on Graph Intell...· 1 citation
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.
Longzhu He, Peng Tang, Chaozhuo Li et al.· IEEE Transactions on Knowled...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
