PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

1 code implementation • 4 Jun 2024 • Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, Jianxin Wang

To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.

Federated Learning Large Language Model +1

Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates

no code implementations • 29 Jun 2019 • Jue Hou, Jelena Bradic, Ronghui Xu

Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences.

Management valid +1

Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference

no code implementations • 29 Jul 2017 • Jue Hou, Jelena Bradic, Ronghui Xu

The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size.