Search Results for author: Zhengyang Zhou

Found 5 papers, 1 papers with code

Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation

1 code implementation29 May 2023 Yifei Wang, Zhengyang Zhou, Liqin Wang, John Laurentiev, Peter Hou, Li Zhou, Pengyu Hong

The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation.

Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective

no code implementations27 Jan 2023 Xu Wang, Pengfei Gu, Pengkun Wang, Binwu Wang, Zhengyang Zhou, Lei Bai, Yang Wang

In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design strategies, which are viewed as the key of enhancing performance on graph learning, are largely ineffective.

Graph Learning

Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics

no code implementations17 Aug 2022 Zhengyang Zhou, Yang Kuo, Wei Sun, Binwu Wang, Min Zhou, Yunan Zong, Yang Wang

To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them.

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

no code implementations9 Feb 2021 Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, Yuantao Li

The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests.

RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

no code implementations19 Feb 2020 Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, Hengchang Liu

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e. g., real-time safe route planning and emergency response deployment).


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