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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...
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09/09/2023
Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces
Recent studies propose enhancing machine learning models by aligning the...
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08/27/2023
Hypergraph Structure Inference From Data Under Smoothness Prior
Hypergraphs are important for processing data with higher-order relation...
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08/23/2023
Learning to Learn Financial Networks for Optimising Momentum Strategies
Network momentum provides a novel type of risk premium, which exploits t...
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08/22/2023
Network Momentum across Asset Classes
We investigate the concept of network momentum, a novel trading signal d...
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08/01/2023
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
We present a novel methodology for modeling and forecasting multivariate...
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06/20/2023
Structure-Aware Robustness Certificates for Graph Classification
Certifying the robustness of a graph-based machine learning model poses ...
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06/08/2023
Bayesian Optimisation of Functions on Graphs
The increasing availability of graph-structured data motivates the task ...
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06/06/2023
Graph Classification Gaussian Processes via Spectral Features
Graph classification aims to categorise graphs based on their structure ...
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05/13/2023
DRew: Dynamically Rewired Message Passing with Delay
Message passing neural networks (MPNNs) have been shown to suffer from t...
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12/09/2022
Understanding stock market instability via graph auto-encoders
Understanding stock market instability is a key question in financial ma...
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11/28/2022
Transductive Kernels for Gaussian Processes on Graphs
Kernels on graphs have had limited options for node-level problems. To a...
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11/03/2022
Learning Hypergraphs From Signals With Dual Smoothness Prior
The construction of a meaningful hypergraph topology is the key to proce...
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10/31/2022
Unrolled Graph Learning for Multi-Agent Collaboration
Multi-agent learning has gained increasing attention to tackle distribut...
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06/16/2022
Learning to Infer Structures of Network Games
Strategic interactions between a group of individuals or organisations c...
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03/29/2022
Graph similarity learning for change-point detection in dynamic networks
Dynamic networks are ubiquitous for modelling sequential graph-structure...
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01/12/2022
Local2Global: A distributed approach for scaling representation learning on graphs
We propose a decentralised "local2global"' approach to graph representat...
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11/29/2021
Understanding over-squashing and bottlenecks on graphs via curvature
Most graph neural networks (GNNs) use the message passing paradigm, in w...
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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...
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11/04/2021
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide ran...
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10/25/2021
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Graph-based models require aggregating information in the graph from nei...
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10/19/2021
Learning to Learn Graph Topologies
Learning a graph topology to reveal the underlying relationship between ...
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10/18/2021
Beltrami Flow and Neural Diffusion on Graphs
We propose a novel class of graph neural networks based on the discretis...
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07/26/2021
Local2Global: Scaling global representation learning on graphs via local training
We propose a decentralised "local2global" approach to graph representati...
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02/18/2021
Interpretable Stability Bounds for Spectral Graph Filters
Graph-structured data arise in a variety of real-world context ranging f...
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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 ...
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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...
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07/31/2020
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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06/13/2020
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel
Bayesian optimisation (BO) has been widely used for hyperparameter optim...
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06/12/2020
Gaussian Processes on Graphs via Spectral Kernel Learning
We propose a graph spectrum-based Gaussian process for prediction of sig...
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12/19/2019
A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learnin...
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07/12/2019
Laplacian-regularized graph bandits: Algorithms and theoretical analysis
We study contextual multi-armed bandit problems in the case of multiple ...
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02/11/2019
Error Analysis on Graph Laplacian Regularized Estimator
We provide a theoretical analysis of the representation learning problem...
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11/21/2018
Learning Quadratic Games on Networks
Individuals, or organizations, cooperate with or compete against one ano...
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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...
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06/03/2018
Learning Graphs from Data: A Signal Representation Perspective
The construction of a meaningful graph topology plays a crucial role in ...
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04/18/2018
Entropic Spectral Learning in Large Scale Networks
We present a novel algorithm for learning the spectral density of large ...
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11/04/2016
Learning heat diffusion graphs
Effective information analysis generally boils down to properly identify...
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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...
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06/30/2014
Learning Laplacian Matrix in Smooth Graph Signal Representations
The construction of a meaningful graph plays a crucial role in the succe...
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04/25/2014
Multiscale Event Detection in Social Media
Event detection has been one of the most important research topics in so...
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03/09/2013
Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
Relationships between entities in datasets are often of multiple nature,...
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06/11/2011
