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.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…
cs.LG2025
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,…