5 papers
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
Meitong Liu, Christopher Jung, Rui Li +2
In transfer learning, the learner leverages auxiliary data to improve generalization on a main task. However, the precise theoretical understanding of when and how auxiliary data h…
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
Xue Feng, Li Wang, Deanna Needell +1
The Jordan-Kinderlehrer-Otto (JKO) scheme provides a stable variational framework for computing Wasserstein gradient flows, but its practical use is often limited by the high compu…
Convergence Analysis of the Alternating Anderson-Picard Method for Nonlinear Fixed-point Problems
Xue Feng, M. Paul Laiu, Thomas Strohmer
Anderson Acceleration (AA) has been widely used to solve nonlinear fixed-point problems due to its rapid convergence. This work focuses on a variant of AA in which multiple Picard…
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng, M. Paul Laiu, Thomas Strohmer
Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data…
Improving Autoencoder Image Interpolation via Dynamic Optimal Transport
Xue Feng, Thomas Strohmer
Autoencoders are important generative models that, among others, have the ability to interpolate image sequences. However, interpolated images are usually not semantically meaningf…