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\title{Bayesian and Active Learning for Graph Inference}
\author{Thibaut Horel \and Jean Pouget-Abadie}

\begin{document}

\maketitle
\begin{abstract}
    The Network Inference Problem (NIP) is the machine learning challenge of
    recovering the edges and edge weights of an unknown weighted graph from the
    observations of a random contagion process propagating over this graph.
    While previous work has focused on provable convergence guarantees for the
    Maximum-Likelihood estimator for the edge weights, a Bayesian treatment of
    the problem is still lacking. In this work, we establish a scalable Bayesian
    framework for the unified NIP formulation of \cite{pouget}. Furthermore, we
    show how this Bayesian framework leads to intuitive and effective heuristics
    to greatly speed up learning.
\end{abstract}

\section{Introduction}
\input{sections/intro.tex}

\section{Model}
\label{sec:model}
\input{sections/model.tex}

\section{Bayesian Inference}
\label{sec:bayes}
\input{sections/bayesian.tex}

\section{Active Learning}
\label{sec:active}
\input{sections/active.tex}

\section{Experiments}
\input{sections/experiments.tex}

\section{Discussion}

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\section{Appendix}
\label{sec:appendix}
\input{sections/appendix.tex}

\end{document}