From 1b76bfe6f4c0335fcf04a9f683949a3d412e05cd Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Thu, 5 Feb 2015 20:54:19 -0500
Subject: adding ref

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 paper/sections/discussion.tex | 5 -----
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@@ -11,11 +11,6 @@ Solving the Graph Inference problem with sparse recovery techniques opens new ve
 
 This model therefore falls into the 1-bit compressed sensing model \cite{Boufounos:2008} framework. Several recent papers study the theoretical guarantees obtained for 1-bit compressed sensing with specific measurements \cite{Gupta:2010}, \cite{Plan:2014}. Whilst they obtained bounds of the order ${\cal O}(n \log \frac{n}{s}$), no current theory exists for recovering positive bounded signals from bernoulli hyperplanes. This research direction may provide the first clues to solve the ``active learning'' problem: if we are allowed to adaptively \emph{choose} the source nodes at the beginning of each cascade, can we improve on current results?
 
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 \begin{comment}
 The Linear Threshold model can \emph{also} be cast a generalized linear cascade model. However, as we show below, its link function is non-differentiable and necessitates a different analysis. In the Linear Threshold Model, each node $j\in V$ has a threshold $t_j$ from the interval $[0,1]$ and for each node, the sum of incoming weights is less than $1$: $\forall j\in V$, $\sum_{i=1}^m \Theta_{i,j} \leq 1$. 
 
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