16 citations · 28 across the 18 of their papers we have counts for
19 papers
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Yingjie Zhou, Yuqin Xie, Fanxing Liu +3
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of…
Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis
Xuyang Shen, Haoran Liu, Dongjin Song +1
Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient infor…
Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
Xuyang Shen, Zijie Pan, Diego Cerrai +4
Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These out…
Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data
Yushan Jiang, Shuteng Niu, Dongjin Song +5
Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods t…
Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
Kanghui Ning, Zijie Pan, Yushan Jiang +3
Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. T…
FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization
Tianyang Luo, Xikun Zhang, Dongjin Song
Why do trillion-dollar tech giants AAPL and MSFT diverge into different response patterns during market disruptions despite identical sector classifications? This paradox reveals a…
Continual Learning to Generalize Forwarding Strategies for Diverse Mobile Wireless Networks
Cheonjin Park, Victoria Manfredi, Xiaolan Zhang +5
Deep reinforcement learning (DRL) has been successfully used to design forwarding strategies for multi-hop mobile wireless networks. While such strategies can be used directly for…
Adaptive von Mises-Fisher Likelihood Loss for Supervised Deep Time Series Hashing
Juan Manuel Perez, Kevin Garcia, Brooklyn Berry +2
Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effectiv…
HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
Guiquan Sun, Xikun Zhang, Jingchao Ni +1
Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data. However, most existing studies…
Multi-modal Time Series Analysis: A Tutorial and Survey
Yushan Jiang, Kanghui Ning, Zijie Pan +7
Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, i…
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning, Zijie Pan, Yu Liu +7
Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they of…
TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee +5
Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…
Learning System Dynamics without Forgetting
Xikun Zhang, Dongjin Song, Yushan Jiang +2
Observation-based trajectory prediction for systems with unknown dynamics is essential in fields such as physics and biology. Most existing approaches are limited to learning withi…
IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Zijie Pan, Yushan Jiang, Sahil Garg +3
Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, establ…
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Zijie Pan, Yushan Jiang, Dongjin Song +4
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting per…
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
Xikun Zhang, Dongjin Song, Dacheng Tao
Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequenti…
Empowering Time Series Analysis with Large Language Models: A Survey
Yushan Jiang, Zijie Pan, Xikun Zhang +4
Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, complet…
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
Jiahui Zhao, Ziyi Meng, Stepan Gordeev +4
With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction…
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
Xikun Zhang, Dongjin Song, Yixin Chen +1
Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networ…