5 papers
Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems
Yue Sun, Likai Wang, Rick S. Blum +1
Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learnin…
Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems
Yue Sun, Rick S. Blum, Parv Venkitasubramaniam
Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper…
Wasserstein-regularized Conformal Prediction under General Distribution Shift
Rui Xu, Chao Chen, Yue Sun +2
Conformal prediction yields a prediction set with guaranteed coverage of the true target under the i.i.d. assumption, which may not hold and lead to a gap between and t…
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
Yue Sun, Chao Chen, Yuesheng Xu +3
Ensuring both accuracy and robustness in time series prediction is critical to many applications, ranging from urban planning to pandemic management. With sufficient training data…
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
Rui Xu, Yue Sun, Chao Chen +2
Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cove…