b'Xiaowen Dong'

research

∙ 09/09/2023

Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization

Recent research indicates that the performance of machine learning model...

0 Haitz Saez de Ocariz Borde, et al. ∙

research

∙ 09/09/2023

Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces

Recent studies propose enhancing machine learning models by aligning the...

0 Haitz Saez de Ocariz Borde, et al. ∙

research

∙ 08/27/2023

Hypergraph Structure Inference From Data Under Smoothness Prior

Hypergraphs are important for processing data with higher-order relation...

0 Bohan Tang, et al. ∙

research

∙ 08/23/2023

Learning to Learn Financial Networks for Optimising Momentum Strategies

Network momentum provides a novel type of risk premium, which exploits t...

0 Xingyue Pu, et al. ∙

research

∙ 08/22/2023

Network Momentum across Asset Classes

We investigate the concept of network momentum, a novel trading signal d...

0 Xingyue Pu, et al. ∙

research

∙ 08/01/2023

Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects

We present a novel methodology for modeling and forecasting multivariate...

0 Chao Zhang, et al. ∙

research

∙ 06/20/2023

Structure-Aware Robustness Certificates for Graph Classification

Certifying the robustness of a graph-based machine learning model poses ...

0 Pierre Osselin, et al. ∙

research

∙ 06/08/2023

Bayesian Optimisation of Functions on Graphs

The increasing availability of graph-structured data motivates the task ...

14 Xingchen Wan, et al. ∙

research

∙ 06/06/2023

Graph Classification Gaussian Processes via Spectral Features

Graph classification aims to categorise graphs based on their structure ...

0 Felix L. Opolka, et al. ∙

research

∙ 05/13/2023

DRew: Dynamically Rewired Message Passing with Delay

Message passing neural networks (MPNNs) have been shown to suffer from t...

0 Benjamin Gutteridge, et al. ∙

research

∙ 12/09/2022

Understanding stock market instability via graph auto-encoders

Understanding stock market instability is a key question in financial ma...

0 Dragos Gorduza, et al. ∙

research

∙ 11/28/2022

Transductive Kernels for Gaussian Processes on Graphs

Kernels on graphs have had limited options for node-level problems. To a...

0 Yin-Cong Zhi, et al. ∙

research

∙ 11/03/2022

Learning Hypergraphs From Signals With Dual Smoothness Prior

The construction of a meaningful hypergraph topology is the key to proce...

0 Bohan Tang, et al. ∙

research

∙ 10/31/2022

Unrolled Graph Learning for Multi-Agent Collaboration

Multi-agent learning has gained increasing attention to tackle distribut...

0 Enpei Zhang, et al. ∙

research

∙ 06/16/2022

Learning to Infer Structures of Network Games

Strategic interactions between a group of individuals or organisations c...

26 Emanuele Rossi, et al. ∙

research

∙ 03/29/2022

Graph similarity learning for change-point detection in dynamic networks

Dynamic networks are ubiquitous for modelling sequential graph-structure...

9 Déborah Sulem, et al. ∙

research

∙ 01/12/2022

Local2Global: A distributed approach for scaling representation learning on graphs

We propose a decentralised "local2global"' approach to graph representat...

34 Lucas G. S. Jeub, et al. ∙

research

∙ 11/29/2021

Understanding over-squashing and bottlenecks on graphs via curvature

Most graph neural networks (GNNs) use the message passing paradigm, in w...

1 Jake Topping, et al. ∙

research

∙ 11/23/2021

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

While Graph Neural Networks (GNNs) have recently become the de facto sta...

17 Emanuele Rossi, et al. ∙

research

∙ 11/04/2021

Adversarial Attacks on Graph Classification via Bayesian Optimisation

Graph neural networks, a popular class of models effective in a wide ran...

14 Xingchen Wan, et al. ∙

research

∙ 10/25/2021

Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets

Graph-based models require aggregating information in the graph from nei...

0 Felix L. Opolka, et al. ∙

research

∙ 10/19/2021

Learning to Learn Graph Topologies

Learning a graph topology to reveal the underlying relationship between ...

0 Xingyue Pu, et al. ∙

research

∙ 10/18/2021

Beltrami Flow and Neural Diffusion on Graphs

We propose a novel class of graph neural networks based on the discretis...

74 Benjamin Paul Chamberlain, et al. ∙

research

∙ 07/26/2021

Local2Global: Scaling global representation learning on graphs via local training

We propose a decentralised "local2global" approach to graph representati...

2 Lucas G. S. Jeub, et al. ∙

research

∙ 02/18/2021

Interpretable Stability Bounds for Spectral Graph Filters

Graph-structured data arise in a variety of real-world context ranging f...

7 Henry Kenlay, et al. ∙

research

∙ 10/26/2020

On the Stability of Graph Convolutional Neural Networks under Edge Rewiring

Graph neural networks are experiencing a surge of popularity within the ...

12 Henry Kenlay, et al. ∙

research

∙ 08/23/2020

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

The problem of graph learning concerns the construction of an explicit t...

40 Xingyue Pu, et al. ∙

research

∙ 07/31/2020

Graph signal processing for machine learning: A review and new perspectives

The effective representation, processing, analysis, and visualization of...

46 Xiaowen Dong, et al. ∙

research

∙ 06/13/2020

Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel

Bayesian optimisation (BO) has been widely used for hyperparameter optim...

0 Binxin Ru, et al. ∙

research

∙ 06/12/2020

Gaussian Processes on Graphs via Spectral Kernel Learning

We propose a graph spectrum-based Gaussian process for prediction of sig...

0 Yin-Cong Zhi, et al. ∙

research

∙ 12/19/2019

A Maximum Entropy approach to Massive Graph Spectra

Graph spectral techniques for measuring graph similarity, or for learnin...

20 Diego Granziol, et al. ∙

research

∙ 07/12/2019

Laplacian-regularized graph bandits: Algorithms and theoretical analysis

We study contextual multi-armed bandit problems in the case of multiple ...

0 Kaige Yang, et al. ∙

research

∙ 02/11/2019

Error Analysis on Graph Laplacian Regularized Estimator

We provide a theoretical analysis of the representation learning problem...

0 Kaige Yang, et al. ∙

research

∙ 11/21/2018

Learning Quadratic Games on Networks

Individuals, or organizations, cooperate with or compete against one ano...

4 Yan Leng, et al. ∙

research

∙ 07/02/2018

Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey

The Data for Refugees (D4R) Challenge is a non-profit challenge initiate...

0 Albert Ali Salah, et al. ∙

research

∙ 06/03/2018

Learning Graphs from Data: A Signal Representation Perspective

The construction of a meaningful graph topology plays a crucial role in ...

0 Xiaowen Dong, et al. ∙

research

∙ 04/18/2018

Entropic Spectral Learning in Large Scale Networks

We present a novel algorithm for learning the spectral density of large ...

0 Diego Granziol, et al. ∙

research

∙ 11/04/2016

Learning heat diffusion graphs

Effective information analysis generally boils down to properly identify...

0 Dorina Thanou, et al. ∙

research

∙ 07/12/2016

Multi-modal image retrieval with random walk on multi-layer graphs

The analysis of large collections of image data is still a challenging p...

0 Renata Khasanova, et al. ∙

research

∙ 06/30/2014

Learning Laplacian Matrix in Smooth Graph Signal Representations

The construction of a meaningful graph plays a crucial role in the succe...

0 Xiaowen Dong, et al. ∙

research

∙ 04/25/2014

Multiscale Event Detection in Social Media

Event detection has been one of the most important research topics in so...

0 Xiaowen Dong, et al. ∙

research

∙ 03/09/2013

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Relationships between entities in datasets are often of multiple nature,...

0 Xiaowen Dong, et al. ∙

research

∙ 06/11/2011

Clustering with Multi-Layer Graphs: A Spectral Perspective

Observational data usually comes with a multimodal nature, which means t...

0 Xiaowen Dong, et al. ∙

Xiaowen Dong

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