no code implementations • 18 Apr 2024 • Kun Zhai, Yifeng Gao, Xingjun Ma, Difan Zou, Guangnan Ye, Yu-Gang Jiang
In this paper, we study the convergence of FL on non-IID data and propose a novel \emph{Dog Walking Theory} to formulate and identify the missing element in existing research.
no code implementations • 7 Mar 2024 • Aosong Feng, Jialin Chen, Juan Garza, Brooklyn Berry, Francisco Salazar, Yifeng Gao, Rex Ying, Leandros Tassiulas
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
1 code implementation • 4 Jan 2023 • Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin
During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner.
no code implementations • 3 Nov 2022 • Li Zhang, Yan Zhu, Yifeng Gao, Jessica Lin
Inspired by a recent work that tracks how the nearest neighbor of a time series subsequence changes over time, we introduce a new TSC definition which is much more robust to noise in the data, in the sense that they can better locate the evolving patterns while excluding the non-evolving ones.
no code implementations • 31 Jan 2022 • Mahyar Shirvanimoghaddam, Ayoob Salari, Yifeng Gao, Aradhika Guha
We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error.
1 code implementation • 30 Jan 2020 • Li Zhang, Yifeng Gao, Jessica Lin
Finding anomalous subsequence in a long time series is a very important but difficult problem.
no code implementations • 29 Jan 2020 • Yifeng Gao, Jessica Lin, Constantin Brif
We demonstrate that the proposed ensemble approach can outperform existing grammar-induction-based approaches with different criteria for selection of parameter values.
1 code implementation • 20 Nov 2019 • Yifeng Gao, Jessica Lin
Despite the significant progress that has been made in recent single dimensional variable-length motif discovery work, detecting variable-length \textit{subdimensional motifs}---patterns that are simultaneously occurring only in a subset of dimensions in multivariate time series---remains a difficult task.