85 citations · 419 across the 56 of their papers we have counts for
29 papers · 1 filter
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
Dongjie Wang, Meng Xiao, Min Wu +3
Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the…
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Meng Xiao, Dongjie Wang, Min Wu +4
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse o…
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
Dongjie Wang, Zhengzhang Chen, Yanjie Fu +2
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system fail…
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
Wei Fan, Pengyang Wang, Dongkun Wang +3
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards dis…
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
Ehtesamul Azim, Dongjie Wang, Yanjie Fu
Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignor…
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
Mingzhe Liu, Han Huang, Hao Feng +3
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in…
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni +4
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal re…
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection
Meng Xiao, Dongjie Wang, Min Wu +3
The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks. Nevertheless, current f…
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Meng Xiao, Dongjie Wang, Min Wu +5
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data…
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Dongkun Wang, Wei Fan, Pengyang Wang +4
Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial corr…
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
Han Huang, Leilei Sun, Bowen Du +2
Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…
Kernel-based Substructure Exploration for Next POI Recommendation
Wei Ju, Yifang Qin, Ziyue Qiao +4
Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important rol…
Self-Optimizing Feature Transformation
Meng Xiao, Dongjie Wang, Min Wu +4
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality,…
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Liangzhe Han, Xiaojian Ma, Leilei Sun +4
Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand…
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
Junchen Ye, Zihan Liu, Bowen Du +4
Recent studies have shown great promise in applying graph neural networks for multivariate time series forecasting, where the interactions of time series are described as a graph s…
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction
Dongjie Wang, Yanjie Fu, Kunpeng Liu +2
Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extrac…
Semi-supervised Drifted Stream Learning with Short Lookback
Weijieying Ren, Pengyang Wang, Xiaolin Li +2
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not alway…
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Wei Fan, Kunpeng Liu, Hao Liu +3
Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards…
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi +4
Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. Howe…
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
Kunpeng Liu, Pengfei Wang, Dongjie Wang +3
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES)…
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Dongjie Wang, Kunpeng Liu, David Mohaisen +3
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) use…
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
Meng Xiao, Ziyue Qiao, Yanjie Fu +2
To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal cla…
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Junchen Ye, Leilei Sun, Bowen Du +2
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station…
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
Wei Fan, Kunpeng Liu, Hao Liu +3
We study the problem of balancing effectiveness and efficiency in automated feature selection. After exploring many feature selection methods, we observe a computational dilemma: 1…