7 papers · 1 filter
Domain Generalization under Sampling Pattern Shifts in Irregular Time Series
Changhun Kim, Joohyung Lee, Kwanhyung Lee +3
Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across…
TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
Hyeongwon Jang, Gyouk Chu, Changhun Kim +4
Clinical early warning systems built on irregularly sampled medical time series (ISMTS) from electronic health records must deliver continuous risk scores for patient triage as wel…
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
Joohyung Lee, Kwanhyung Lee, Changhun Kim +1
Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, t…
Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Changhun Kim, Yechan Mun, Hyeongwon Jang +3
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin criti…
DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values
Changhun Kim, Yechan Mun, Sangchul Hahn +1
This study proposes DeltaSHAP, a novel explainable artificial intelligence (XAI) algorithm specifically designed for online patient monitoring systems. In clinical environments, di…
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Hyeongwon Jang, Changhun Kim, Eunho Yang
Recent explainable artificial intelligence (XAI) methods for time series primarily estimate point-wise attribution magnitudes, while overlooking the directional impact on predictio…
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
Changhun Kim, Taewon Kim, Seungyeon Woo +2
In real-world scenarios, tabular data often suffer from distribution shifts that threaten the performance of machine learning models. Despite its prevalence and importance, handlin…