1 citations · 1 across the 4 of their papers we have counts for
6 papers
Quickest Change-point Detection in Generalized Linear Models with Variational Estimation
Jonghyeok Lee, Yao Xie
We consider quickest change-point detection in generalized linear models when the post-change parameter is unknown. Such problems arise, for example, when monitoring whether the re…
Conformal Prediction for Time Series with Deep Sequence Models
Junghwan Lee, Jonghyeok Lee, Yao Xie
Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has gained attention as a distribu…
Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
Linglingzhi Zhu, Jonghyeok Lee, Yao Xie
Generalized linear models (GLMs) are fundamental tools for statistical modeling, with maximum likelihood estimation (MLE) serving as the classical approach for parameter inference.…
Multi-rank Subspace Change-point Detection with Application in Monitoring Robotic Swarms
Jonghyeok Lee, Yao Xie, Youngser Park +3
We study real-time detection of low-rank changes in the covariance structure of high-dimensional streaming data, motivated by robotic swarm monitoring. Building on the spiked covar…
Kernel-based Optimally Weighted Conformal Time-Series Prediction
Jonghyeok Lee, Chen Xu, Yao Xie
In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, K…
Flow-based Distributionally Robust Optimization
Chen Xu, Jonghyeok Lee, Xiuyuan Cheng +1
We present a computationally efficient framework, called , for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty…