… Motivated by this, in this paper, we consider both efficiency and trustworthiness … We distill
both InD and the generated outliers into a tiny dataset, such a distilled dataset is capable of …

… propose a trustworthy benchmark for assessing representative dataset distillation solutions
… vulnerabilities inherent in the application of dataset distillation, offering valuable insights for …

… as a good surrogate for labeled dataset. A naive way to … Trustworthy AL) that enables the
training of consistent acquisition model that serves as a good reference for labeled dataset …

… guidance of the teacher model in distillation is reliable or not. … distillation is not always
trustworthy. Formally, adversarial … data during adversarial distillation on CIFAR-10 dataset. Noted …

… Trustworthy risk predictions with causal interpretability and high precision and recall can be
… on distilled data. We evaluate our TDCFD method on several synthetic and real datasets, …

… Towards this direction, a novel technique known as Fed… IID dataset. A prevalent approach
in the literature involves the use of a public dataset accessible to all clients for local distillation, …

… In conclusion, the superiority of FEDMIC under different medical image classification
datasets comes from the dual knowledge distillation mechanism and global parameter …

… against malicious inputs, making it a crucial aspect of trustworthy machine learning. Given
the potential of dataset distillation to safeguard the privacy of the original dataset (Geng et al., …

… in compressing datasets, there are still several limitations such as distilling … dataset distillation
from multiple aspects, including distillation frameworks and algorithms, factorized dataset …

… calibrate networks trained on data distilled from large source datasets. In this paper, we
show that … Toward trustworthy programming for autonomous concurrent systems. AI & SOCIETY, …