3 papers
cs.LG2026
Robust Losses from Univariate Base Functions for Noisy-Label Learning
Peng Hu, Jianwei Ma
Learning with noisy labels is a fundamental problem in training reliable deep neural networks. Robust loss functions provide a direct and effective way to mitigate the adverse effe…
cs.LG2026
Personalized Federated Learning via Gaussian Generative Modeling
Peng Hu, Jianwei Ma
Federated learning has emerged as a paradigm to train models collaboratively on inherently distributed client data while safeguarding privacy. In this context, personalized federat…
cs.CV2025
Conditional Representation Learning for Customized Tasks
Honglin Liu, Chao Sun, Peng Hu +2
Conventional representation learning methods learn a universal representation that primarily captures dominant semantics, which may not always align with customized downstream task…