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04/21/2023
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs
We study the convergence of message passing graph neural networks on ran...
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10/31/2022
A Faster Sampler for Discrete Determinantal Point Processes
Discrete Determinantal Point Processes (DPPs) have a wide array of poten...
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10/15/2022
Smoothing complex-valued signals on Graphs with Monte-Carlo
We introduce new smoothing estimators for complex signals on graphs, bas...
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01/04/2022
Gaussian Process Regression in the Flat Limit
Gaussian process (GP) regression is a fundamental tool in Bayesian stati...
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07/15/2021
Determinantal Point Processes in the Flat Limit
Determinantal point processes (DPPs) are repulsive point processes where...
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07/12/2021
Extended L-ensembles: a new representation for Determinantal Point Processes
Determinantal point processes (DPPs) are a class of repulsive point proc...
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03/05/2021
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
This article unveils a new relation between the Nishimori temperature pa...
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11/20/2020
Graph Tikhonov Regularization and Interpolation via Random Spanning Forests
Novel Monte Carlo estimators are proposed to solve both the Tikhonov reg...
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10/16/2020
Fast Graph Kernel with Optical Random Features
The graphlet kernel is a classical method in graph classification. It ho...
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06/03/2020
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
This article considers the problem of community detection in sparse dyna...
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03/20/2020
A unified framework for spectral clustering in sparse graphs
This article considers spectral community detection in the regime of spa...
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12/03/2019
Optimal Laplacian regularization for sparse spectral community detection
Regularization of the classical Laplacian matrices was empirically shown...
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10/17/2019
Smoothing graph signals via random spanning forests
Another facet of the elegant link between random processes on graphs and...
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05/06/2019
Estimating the inverse trace using random forests on graphs
Some data analysis problems require the computation of (regularised) inv...
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01/29/2019
Approximating Spectral Clustering via Sampling: a Review
Spectral clustering refers to a family of unsupervised learning algorith...
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01/25/2019
Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs
Spectral clustering is one of the most popular, yet still incompletely u...
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03/23/2018
Determinantal Point Processes for Coresets
When one is faced with a dataset too large to be used all at once, an ob...
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03/05/2018
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
Determinantal Point Processes (DPPs) are popular models for point proces...
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02/23/2018
Optimized Algorithms to Sample Determinantal Point Processes
In this technical report, we discuss several sampling algorithms for Det...
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11/01/2017
Analyzing the Approximation Error of the Fast Graph Fourier Transform
The graph Fourier transform (GFT) is in general dense and requires O(n^2...
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03/05/2017
Graph sampling with determinantal processes
We present a new random sampling strategy for k-bandlimited signals defi...
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10/27/2016
Compressive K-means
The Lloyd-Max algorithm is a classical approach to perform K-means clust...
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02/05/2016
Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high perfo...
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11/16/2015
