Portrait de Mansi Rankawat

Mansi Rankawat

Doctorat - UdeM
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Optimisation
Théorie de l'apprentissage automatique
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Google Scholar
GitHub

Publications

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