31 citations · 41 across the 9 of their papers we have counts for
10 papers
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
Kailai Sun, Mingyi He, Heye Huang +5
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery a…
TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
Xinyu Chen, HanQin Cai, Lijun Ding +1
We present TailedTS, a large-scale benchmark dataset derived from Wikipedia hourly page view observations throughout 2024, specifically designed to test time series forecasting mod…
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion
Baoshen Guo, Zhiqing Hong, Junyi Li +2
Urban mobility data has significant connections with economic growth and plays an essential role in various smart-city applications. However, due to privacy concerns and substantia…
Virtual Nodes Improve Long-term Traffic Prediction
Xiaoyang Cao, Dingyi Zhuang, Jinhua Zhao +1
Effective traffic prediction is a cornerstone of intelligent transportation systems, enabling precise forecasts of traffic flow, speed, and congestion. While traditional spatio-tem…
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng +2
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly p…
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Dingyi Zhuang, Yuheng Bu, Guang Wang +2
Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the i…
Correlating Time Series with Interpretable Convolutional Kernels
Xinyu Chen, HanQin Cai, Fuqiang Liu +1
This study addresses the problem of convolutional kernel learning in univariate, multivariate, and multidimensional time series data, which is crucial for interpreting temporal pat…
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
Shenhao Wang, Baichuan Mo, Yunhan Zheng +2
Numerous studies have compared machine learning (ML) and discrete choice models (DCMs) in predicting travel demand. However, these studies often lack generalizability as they compa…
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Shenhao Wang, Baichuan Mo, Jinhua Zhao
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to impro…