Search Results for author: Xingyue Pu

Found 3 papers, 1 papers with code

Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects

no code implementations1 Aug 2023 Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong

We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks.

Learning to Learn Graph Topologies

1 code implementation NeurIPS 2021 Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen

The model is trained in an end-to-end fashion with pairs of node data and graph samples.

Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint

no code implementations23 Aug 2020 Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic

In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that help to explain the dependency structures in graph signals.

Graph Learning

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