3 papers
math.OC2026
Nesterov acceleration in optimizing over probability measures
Jiaqi Tang, Qin Li, Wilfrid Gangbo
Optimization over probability measures has become an increasingly important paradigm in modern machine learning, scientific computing, and uncertainty quantification. Motivated by…
cs.LG2026
Correcting Auto-Differentiation in Neural-ODE Training
Yewei Xu, Shi Chen, Qin Li
Does the use of auto-differentiation yield reasonable updates for deep neural networks (DNNs)? Specifically, when DNNs are designed to adhere to neural ODE architectures, can we tr…
math.OC2024
Accelerating optimization over the space of probability measures
Shi Chen, Qin Li, Oliver Tse +1
The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While m…