3 papers
cs.AI2026
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Peisong Zhang, Manqiang Peng, Yuxuan Wu +21
Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing co…
cs.LG2025
Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints
Yuxuan Wu, Ziyu Wang, Bhiksha Raj +1
We contribute an unsupervised method that effectively learns disentangled content and style representations from sequences of observations. Unlike most disentanglement algorithms t…
math.OC2024
Formalization of Complexity Analysis of the First-order Algorithms for Convex Optimization
Chenyi Li, Ziyu Wang, Wanyi He +3
The convergence rate of various first-order optimization algorithms is a pivotal concern within the numerical optimization community, as it directly reflects the efficiency of thes…