6 papers
Adaptive COVID-19 Trajectory Forecasting Using MAB-Inspired Ensemble Weighting
Hamed Karami, Javier Redondo Anton, Geunsoo Jang +2
Forecasting epidemic trajectories is important for public health decision-making, but no single model is consistently reliable across epidemic phases and forecasting settings. We e…
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu +2
Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is obse…
Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models
Preetom Biswas, Giulia Pedrielli, K. Selçuk Candan
Inferring causal relations in timeseries data with delayed effects is a fundamental challenge, especially when the underlying system exhibits complex dynamics that cannot be captur…
Causality Guided Representation Learning for Cross-Style Hate Speech Detection
Chengshuai Zhao, Shu Wan, Paras Sheth +3
The proliferation of online hate speech poses a significant threat to the harmony of the web. While explicit hate is easily recognized through overt slurs, implicit hate speech is…
CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
Shu Wan, Reepal Shah, John Sabo +2
Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook und…
Cross-Domain Conditional Diffusion Models for Time Series Imputation
Kexin Zhang, Baoyu Jing, K. Selçuk Candan +4
Cross-domain time series imputation is an underexplored data-centric research task that presents significant challenges, particularly when the target domain suffers from high missi…