3 papers
cs.AI2026
DWM: Separating World Effects from Actions in Latent World Models
Yi-Ge Zhang, Tianqi Du, Qi Zhang +1
Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated ta…
cs.LG2026
Difficult Examples Hurt Unsupervised Contrastive Learning: A Theoretical Perspective
Yi-Ge Zhang, Jingyi Cui, Qiran Li +1
Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However,…
cs.LG2025
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise
Jingyi Cui, Yi-Ge Zhang, Hengyu Liu +1
Learning from noisy labels is a critical challenge in machine learning, with vast implications for numerous real-world scenarios. While supervised contrastive learning has recently…