📝 Publications

Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses
Xiaolin Hu, Shaojie Li, Yong Liu

Video

• This paper provides a theoretical analysis of generalization error of federated learning.
• We assume that the heterogeneous clients are sampled from a meta-distribution. In this framework, we characterize the generalization error for unparticipating clients.
• We further derive convergence bounds for heavy-tail losses.

A Deep Learning Framework for Solving Rectangular Waveguide Problems
Xiaolin Hu, Nicholas E. Buri, APMC 2020 (Oral) |

Project

• We employ Physics Informed Neural Networks (PINNs) to solve rectangular waveguide problems.
• We successfully apply PINNs to the task of solving electric and magnetic fields, which can be described by partial differential equations (PDEs).
• We also show the applicability of the framework for predicting the unknown parameters such as wavenumber.