22 citations · 123 across the 29 of their papers we have counts for
16 papers · 1 filter
Differentiable Zero-One Loss via Hypersimplex Projections
Camilo Gomez, Pengyang Wang, Liansheng Tang
Recent advances in machine learning have emphasized the integration of structured optimization components into end-to-end differentiable models, enabling richer inductive biases an…
Deep Cut-informed Graph Embedding and Clustering
Zhiyuan Ning, Zaitian Wang, Ran Zhang +8
Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However,…
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
Dongjie Wang, Yanyong Huang, Wangyang Ying +11
Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric…
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Yicheng Zhou, Pengfei Wang, Hao Dong +4
Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networ…
A Comprehensive Survey on Data Augmentation
Zaitian Wang, Pengfei Wang, Kunpeng Liu +6
Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models…
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Zhiyuan Ning, Chunlin Tian, Meng Xiao +5
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Tr…
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
Wei Fan, Shun Zheng, Pengyang Wang +5
Due to the non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either rely on using certa…
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
Xuanming Hu, Wei Fan, Dongjie Wang +3
Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern e…
Deep Generative Imputation Model for Missing Not At Random Data
Jialei Chen, Yuanbo Xu, Pengyang Wang +1
Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At…
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…
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…
Graph Soft-Contrastive Learning via Neighborhood Ranking
Zhiyuan Ning, Pengfei Wang, Pengyang Wang +5
Graph Contrastive Learning (GCL) has emerged as a promising approach in the realm of graph self-supervised learning. Prevailing GCL methods mainly derive from the principles of con…
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…
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…
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Wei Fan, Kunpeng Liu, Hao Liu +3
In this paper, we study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is a fundamental intelligence for machine learning a…