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cs.LG2025
AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection
Puning Yang, Jian Liang, Jie Cao +1
Out-of-distribution (OOD) detection aims to detect test samples that do not fall into any training in-distribution (ID) classes. Prior efforts focus on regularizing models with ID…
cs.LG2024
Exploring Vacant Classes in Label-Skewed Federated Learning
Kuangpu Guo, Yuhe Ding, Jian Liang +3
Label skews, characterized by disparities in local label distribution across clients, pose a significant challenge in federated learning. As minority classes suffer from worse accu…
cs.LG2024
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
Jian Liang, Ran He, Tieniu Tan
Machine learning methods strive to acquire a robust model during the training process that can effectively generalize to test samples, even in the presence of distribution shifts.…