4 citations · 12 across the 10 of their papers we have counts for
11 papers
VERAS: Verification-Guided Adaptive Search for Reliable Operations Research
Minbo Ma, Honglin Zhang, Bangtao Zhou +5
Advances in reasoning, coding, and tool use enable LLM agents to solve operations research (OR) problems through iterative problem modeling, program construction, and verification.…
TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data
Minghui Lu, Yanyong Huang, Minbo Ma +4
Multi-view unsupervised feature selection (MUFS), which selects informative features from multi-view unlabeled data, has attracted increasing research interest in recent years. Alt…
Non-collective Calibrating Strategy for Time Series Forecasting
Bin Wang, Yongqi Han, Minbo Ma +4
Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make i…
CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values
Kai Tang, Ji Zhang, Hua Meng +5
Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missin…
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
Minbo Ma, Kai Tang, Huan Li +3
Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyb…
Causally-Aware Unsupervised Feature Selection Learning
Zongxin Shen, Yanyong Huang, Dongjie Wang +3
Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intri…
UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method
Peng Xie, Minbo Ma, Bin Wang +2
Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing ap…
Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting
Qi Xiong, Kai Tang, Minbo Ma +3
Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in exist…
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Minbo Ma, Jilin Hu, Christian S. Jensen +4
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use o…
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
Peng Xie, Minbo Ma, Tianrui Li +4
Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First,…
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
Minbo Ma, Peng Xie, Fei Teng +4
Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time serie…