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cs.LG2026
LeHami: Symplectic Latent Dynamics for World Models
Romina Yalovetzky, Randall Balestriero, Yann LeCun
Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribution over…
stat.ME2026
Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning
Yue Yu, Guanghui Wang, Liu Liu +1
Dimension reduction is a fundamental tool for analyzing high-dimensional data in supervised learning. Traditional methods for estimating intrinsic order often prioritize model-spec…