24 citations · 26 across the 3 of their papers we have counts for
4 papers
Test-time Calibration Learning for Large Language Model Reasoning
Zizhuo Zhang, Xiong Peng, Jingwei Sun +4
Reliable large language models (LLMs) must not only produce accurate answers but also express confidence that faithfully reflects their probability of being correct. Such calibrati…
Generative Model Inversion Through the Lens of the Manifold Hypothesis
Xiong Peng, Bo Han, Fengfei Yu +3
Model inversion attacks (MIAs) aim to reconstruct class-representative samples from trained models. Recent generative MIAs utilize generative adversarial networks to learn image pr…
Model Inversion Attacks: A Survey of Approaches and Countermeasures
Zhanke Zhou, Jianing Zhu, Fengfei Yu +4
Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as graphs. Because these networks…
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Xiong Peng, Feng Liu, Jingfen Zhang +4
Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training dat…