4 citations · 5 across the 8 of their papers we have counts for
8 papers
Hierarchical Discrete Flow Matching for Graph Generation
Yoann Boget, Pablo Strasser, Alexandros Kalousis
Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fu…
Unrestrained Simplex Denoising for Discrete Data. A Non-Markovian Approach Applied to Graph Generation
Yoann Boget, Alexandros Kalousis
Denoising models such as Diffusion or Flow Matching have recently advanced generative modeling for discrete structures, yet most approaches operate directly in the discrete state s…
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
Yoann Boget
Discrete Diffusion and Flow Matching models have significantly advanced generative modeling for discrete structures, including graphs. However, the dependencies between intermediat…
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda +1
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre pe…
Discrete Graph Auto-Encoder
Yoann Boget, Magda Gregorova, Alexandros Kalousis
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique…
GrannGAN: Graph annotation generative adversarial networks
Yoann Boget, Magda Gregorova, Alexandros Kalousis
We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. Th…
Permutation Equivariant Generative Adversarial Networks for Graphs
Yoann Boget, Magda Gregorova, Alexandros Kalousis
One of the most discussed issues in graph generative modeling is the ordering of the representation. One solution consists of using equivariant generative functions, which ensure t…
Adversarial Regression. Generative Adversarial Networks for Non-Linear Regression: Theory and Assessment
Yoann Boget
Adversarial Regression is a proposition to perform high dimensional non-linear regression with uncertainty estimation. We used Conditional Generative Adversarial Network to obtain…