Search Results for author:
Found 8 papers, 3 papers with code
The Dog Walking Theory: Rethinking Convergence in Federated Learning
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.
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series
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.
Robust Time Series Chain Discovery with Incremental Nearest Neighbors
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.
Federated Learning with Erroneous Communication Links
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.
Semantic Discord: Finding Unusual Local Patterns for Time Series
Finding anomalous subsequence in a long time series is a very important but difficult problem.
Ensemble Grammar Induction For Detecting Anomalies in Time Series
We demonstrate that the proposed ensemble approach can outperform existing grammar-induction-based approaches with different criteria for selection of parameter values.
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series
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.
