Portrait de Antonio Gois

Antonio Gois

Doctorat - UdeM
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Causalité
Systèmes multi-agents
Théorie des jeux
Site web
Google Scholar
GitHub

Publications

Performative Prediction on Games and Mechanism Design
Antonio Gois
Mehrnaz Mofakhami
Fernando P. Santos
Gauthier Gidel
Simon Lacoste-Julien
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
openreview.net
Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu
Antonio Gois
Chris Emezue
Mansi Rankawat
Simon Lacoste-Julien
Stefan Bauer
Yoshua Bengio
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian ne… (voir plus)tworks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling of discrete and composite objects, such as graphs. In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. Generating a sample DAG from this approximate distribution is viewed as a sequential decision problem, where the graph is constructed one edge at a time, based on learned transition probabilities. Through evaluation on both simulated and real data, we show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs, and it compares favorably against other methods based on MCMC or variational inference.
2022-05-20
auai.org/UAI/2022/Conference (poster)
proceedings.mlr.press
openreview.net