Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks

1 code implementation • 22 Dec 2023 • Zheyuan Hu, Zekun Shi, George Em Karniadakis, Kenji Kawaguchi

We further showcase HTE's convergence to the original PINN loss and its unbiased behavior under specific conditions.

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

1 code implementation • 12 Nov 2023 • Wenke Huang, Mang Ye, Zekun Shi, Guancheng Wan, He Li, Bo Du, Qiang Yang

In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.

Fairness Federated Learning +1

Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning

2 code implementations • 28 Sep 2023 • Wenke Huang, Mang Ye, Zekun Shi, Bo Du

Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data.

Domain Generalization Federated Learning +1

Rethinking Federated Learning With Domain Shift: A Prototype View

2 code implementations • CVPR 2023 • Wenke Huang, Mang Ye, Zekun Shi, He Li, Bo Du

The private model presents degenerative performance on other domains (with domain shift).

Federated Learning Privacy Preserving

Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

no code implementations • 25 Jan 2019 • Pengqian Yu, Joon Sern Lee, Ilya Kulyatin, Zekun Shi, Sakyasingha Dasgupta

Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile.

Data Augmentation Portfolio Optimization +2