From a08f475a5ace6d069ba4cf0c93c6ef4df2b117b0 Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Fri, 11 Dec 2015 17:11:12 -0500
Subject: experiments section start

---
 finale/final_report.tex | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

(limited to 'finale/final_report.tex')

diff --git a/finale/final_report.tex b/finale/final_report.tex
index 9e1e33c..159badc 100644
--- a/finale/final_report.tex
+++ b/finale/final_report.tex
@@ -7,7 +7,7 @@
 \usepackage{graphicx}
 \usepackage{bbm}
 %\usepackage{fullpage}
-\input{def} 
+\input{def}
 \usepackage{icml2015}
 %\usepackage{algpseudocode}
 \DeclareMathOperator*{\argmax}{arg\,max}
@@ -43,6 +43,18 @@
 \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}
 
@@ -51,14 +63,24 @@
 \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}
+
 \bibliography{sparse}
 \bibliographystyle{icml2015}
+
+\newpage
+\section{Appendix}
+\label{sec:appendix}
+\input{sections/appendix.tex}
+
 \end{document}
-- 
cgit v1.2.3-70-g09d2

