I sometimes write summaries of research ideas and various technical content. It can be found below.
Articles
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The Change Of Variable Formula and the Gaussian Integral
November 30, 2022
In this post, I present a simple way to calculate the Gaussian Integral, that is a very appealing application of the change of variable formula.
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Investigating Different distance function for Latent Distance graph models
October 1, 2022
In this post, we consider Latent Space models for graphs, and investigate the impact of the distance function used on the embedding space.
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About train-test splitting for link prediction
March 25, 2022
In this post, we describe the link prediction task for networks, and the strategies to evaluate link prediction methods
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The Expectation-Maximization algorithm
March 14, 2022
In this post, I explain the popular Expectation-Maximization algorithm under a variational methods perspective.$\newcommand{\set}[1]{\left\{ #1 \right\}}$
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Variational Methods
March 1, 2022
In this post, I give an overview of variational methods in the context of Bayesian inference.
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A visualization of latent space distance models for Graphs
February 1, 2022
In this article I give a visualization of latent space distance models for graphs, and how they allow to disantangle the metric structure of the graph from prior information such as node/edge attributes.
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Conditional Network Embedding, a Latent Space Distance perspective
November 1, 2020
In this article I summarize the Conditional Network Embedding model, and underline its connection with the broader class of Latent Space Distance models for graphs.
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Maximum Entropy models for Graphs
October 1, 2020
In this post, we give an overview Maximum Entropy models for graphs, as presented in previous work (Bie, 2010) and (Adriaens et al., 2017). We show how these models can be used to derive prior distributions on graphs.