From 0fd1178dcffa025772bf5bfc43f24e692596747a Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Tue, 19 May 2015 00:32:39 +0200
Subject: Final compression

---
 paper/sections/intro.tex | 5 +++++
 1 file changed, 5 insertions(+)

(limited to 'paper/sections/intro.tex')

diff --git a/paper/sections/intro.tex b/paper/sections/intro.tex
index c89c641..cc29ed7 100644
--- a/paper/sections/intro.tex
+++ b/paper/sections/intro.tex
@@ -73,21 +73,26 @@ However, the best known upper bound to this day is $\O(s^2\log
 m)$~\cite{Netrapalli:2012, Daneshmand:2014}
 
 The contributions of this paper are the following:
+\vspace{-1em}
 \begin{itemize}
     \item we formulate the Graph Inference problem in the context of
         discrete-time influence cascades as a sparse recovery problem for
         a specific type of Generalized Linear Model. This formulation notably
         encompasses the well-studied Independent Cascade Model and Voter Model.
+        \vspace{-0.5em}
     \item we give an algorithm which recovers the graph's edges using $\O(s\log
         m)$ cascades. Furthermore, we show that our algorithm is also able to
         efficiently recover the edge weights (the parameters of the influence
         model) up to an additive error term,
+        \vspace{-0.5em}
     \item we show that our algorithm is robust in cases where the signal to
         recover is approximately $s$-sparse by proving guarantees in the
         \emph{stable recovery} setting.
+        \vspace{-0.5em}
     \item we provide an almost tight lower bound of $\Omega(s\log \frac{m}{s})$
         observations required for sparse recovery.
 \end{itemize}
+\vspace{-0.5em}
 
 The organization of the paper is as follows: we conclude the introduction by a
 survey of the related work. In Section~\ref{sec:model} we present our model of
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