7 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…
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…
Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach
Zhijian Zhou, Liuhua Peng, Xunye Tian +2
Are two distributions close to each other with statistical significance? Distribution closeness testing (DCT) formalizes this question by testing whether the distance between a dis…
Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting
Yuchen Luo, Xinyu Li, Liuhua Peng +1
In multivariate time series forecasting (MTSF), accurately modeling the intricate dependencies among multiple variables remains a significant challenge due to the inherent limitati…
DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
Zhijian Zhou, Xunye Tian, Liuhua Peng +4
To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-ker…
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…