6 papers
USAD: Uncertainty-aware Statistical Adversarial Detection
Zhijian Zhou, Xunye Tian, Jiacheng Zhang +5
Statistical adversarial detection (SAD) treats detection as a two-sample test. Given a reference set of clean examples (CEs) and a batch of queries, potentially containing an unkno…
CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes
Zhijian Zhou, Zesheng Ye, Zhaorun Chen +2
Can we trust evaluation scores to capture an LLM's true real-world performance? Certifiable evaluation answers this question by providing guarantee for LLM evaluation. In particula…
What Do Deepfake Benchmarks Measure? An Audit Using Frozen Self-Supervised Representations
Samuel Pagon, Yixuan Shen, Vishal Asnani +1
As deepfake generators approach perceptual indistinguishability, reliable detection becomes critical. Yet, detectors that score well on benchmarks routinely fail in the wild. A con…
FedReLa: Imbalanced Federated Learning via Re-Labeling
Guangzheng Hu, Patricia Menéndez, Feng Liu +3
Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity n…
Anchor-based Maximum Discrepancy for Relative Similarity Testing
Zhijian Zhou, Liuhua Peng, Xunye Tian +1
The relative similarity testing aims to determine which of the distributions, P or Q, is closer to an anchor distribution U. Existing kernel-based approaches often test the relativ…
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Xunye Tian, Liuhua Peng, Zhijian Zhou +3
Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn da…