74 citations · 105 across the 7 of their papers we have counts for
7 papers
CORAL: Concept Drift Representation Learning for Co-evolving Time-series
Kunpeng Xu, Lifei Chen, Shengrui Wang
In the realm of time series analysis, tackling the phenomenon of concept drift poses a significant challenge. Concept drift -- characterized by the evolving statistical properties…
Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach
Kunpeng Xu, Lifei Chen, Shengrui Wang
Kernel-based subspace clustering, which addresses the nonlinear structures in data, is an evolving area of research. Despite noteworthy progressions, prevailing methodologies predo…
WormKAN: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?
Kunpeng Xu, Lifei Chen, Shengrui Wang
Dynamic concepts in time series are crucial for understanding complex systems such as financial markets, healthcare, and online activity logs. These concepts help reveal structures…
Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences
Kunpeng Xu, Lifei Chen, Shengrui Wang
Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity lo…
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
Pierre-Daniel Arsenault, Shengrui Wang, Jean-Marc Patenande
Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity ofte…
Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
Kunpeng Xu, Lifei Chen, Shengrui Wang
Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team, representing a revolutionary approach with the potential to be a game-changer in the f…
DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning
Kunpeng Xu, Lifei Chen, Shengrui Wang
Machine learning techniques have outperformed numerous rule-based methods for decision-making in autonomous vehicles. Despite recent efforts, lane changing remains a major challeng…