no code implementations • 21 Jan 2024 • Yinuo Ren, Chao Ma, Lexing Ying
Why do neural networks trained with large learning rates for a longer time often lead to better generalization?
no code implementations • 10 Dec 2023 • Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff
Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in biophysical systems to modeling financial instruments.
no code implementations • 22 Nov 2023 • Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications.
no code implementations • 21 Nov 2023 • Yinuo Ren, Feng Li, Yanfei Kang, Jue Wang
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series.
no code implementations • 1 Dec 2022 • Yinuo Ren, Hongli Zhao, Yuehaw Khoo, Lexing Ying
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data.
no code implementations • 12 Dec 2020 • Haoya Li, Yuehaw Khoo, Yinuo Ren, Lexing Ying
This paper proposes a new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations.