8 citations · 8 across the 5 of their papers we have counts for
5 papers
An Invariant Latent Space Perspective on Language Model Inversion
Wentao Ye, Jiaqi Hu, Haobo Wang +7
Language model inversion (LMI), i.e., recovering hidden prompts from outputs, emerges as a concrete threat to user privacy and system security. We recast LMI as reusing the LLM's o…
Towards Robust Incremental Learning under Ambiguous Supervision
Rui Wang, Mingxuan Xia, Chang Yao +4
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotat…
AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Beibei Li, Yiyuan Zheng, Beihong Jin +3
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models…
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Zhiqing Xiao, Haobo Wang, Ying Jin +4
Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most…
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
Jie Xu, Yazhou Ren, Xiaolong Wang +4
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners. Multiple views provide more information than single views and th…