484 citations · 1.7k across the 48 of their papers we have counts for
8 papers · 1 filter
EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
Ziqian Wang, Tingxiong Xiao, Yuxiao Cheng +1
Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations. We prop…
A Self-explainable Model of Long Time Series by Extracting Informative Structured Causal Patterns
Ziqian Wang, Yuxiao Cheng, Jinli Suo
Explainability is essential for neural networks that model long time series, yet most existing explainable AI methods only produce point-wise importance scores and fail to capture…
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Yuxiao Cheng, Xinxin Song, Ziqian Wang +3
Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney in…
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao +3
Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performan…
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Tingxiong Xiao, Weihang Zhang, Yuxiao Cheng +1
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring makin…
SHoP: A Deep Learning Framework for Solving High-order Partial Differential Equations
Tingxiong Xiao, Runzhao Yang, Yuxiao Cheng +2
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research. Due to…
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng, Lianglong Li, Tingxiong Xiao +4
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researcher…
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao +4
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and hig…