From 4495aab606bfa8cbdd1b465c097b3fa7ee9690fa Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Fri, 6 Nov 2015 12:11:19 -0500
Subject: Add abstract

---
 finale/mid_report.tex | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

(limited to 'finale/mid_report.tex')

diff --git a/finale/mid_report.tex b/finale/mid_report.tex
index 684d0a8..e35a839 100644
--- a/finale/mid_report.tex
+++ b/finale/mid_report.tex
@@ -35,7 +35,7 @@
 \newtheorem*{example}{Example}
 \newtheorem*{remark}{Remark}
 
-\title{Regression Analysis with Network Data}
+\title{Network Inference from Cascades}
 \author{Thibaut Horel \and Jean Pouget-Abadie}
 
 \begin{document}
@@ -43,7 +43,17 @@
 \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 estimators with provable convergence rate guarantees have been
+    obtained under various formulations of the NIP, a rigorous statistical
+    treatment of the problem is still lacking. In this work, we build upon the
+    unified NIP formulation of [] to explore the connections between the
+    topological properties of the graph to be learnt and the resulting quality
+    of the estimators. Specifically, we analyze which properties of the graph
+    render NIP unfeasible or hard, and which properties can be exploited to
+    improve the quality of the estimators.
 \end{abstract}
 
 \section{Introduction}
-- 
cgit v1.2.3-70-g09d2

