4 papers
When Sample Selection Bias Precipitates Model Collapse
Xinbao Qiao, Xianglong Du, Wei Liu +4
The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distributional tails and homogenizes…
An Information-Theoretic Analysis for Federated Learning under Concept Drift
Fu Peng, Meng Zhang, Ming Tang
Recent studies in federated learning (FL) commonly train models on static datasets. However, real-world data often arrives as streams with shifting distributions, causing performan…
Soft Weighted Machine Unlearning
Xinbao Qiao, Ningning Ding, Yushi Cheng +1
Machine unlearning, as a post-hoc processing technique, has gained widespread adoption in addressing challenges like bias mitigation and robustness enhancement, colloquially, machi…
Hessian-Free Online Certified Unlearning
Xinbao Qiao, Meng Zhang, Ming Tang +1
Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storin…