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20192026
most citedAdversarial Regression. Generative Adversarial Networks for Non-Linear Regression: Theory and Assessment

4 citations · 5 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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…

cs.LG2024

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…

cs.LG2023★ 1 cited

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…

cs.LG2022

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…

cs.LG2021

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…