activity
20192026
most citedSAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

5 citations · 9 across the 7 of their papers we have counts for

collaborators

9 papers

cs.AI2026

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…

cs.CV2026

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…

cs.LG2026

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…

cs.SI2025

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…

cs.LG2025

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…

cs.LG2024

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…