Polishing

1 files changed, 3 insertions, 3 deletions

diff --git a/paper/sections/lowerbound.tex b/paper/sections/lowerbound.tex
index fdee069..b714766 100644
--- a/paper/sections/lowerbound.tex
+++ b/paper/sections/lowerbound.tex
@@ -1,5 +1,5 @@
 In this section, we will consider the stable sparse recovery setting of
-Section~\ref{sec:relaxing}.  Our goal is to obtain an information-theoretic
+Section~\ref{sec:relaxing_sparsity}.  Our goal is to obtain an information-theoretic
 lower bound on the number of measurements necessary to approximately recover
 the parameter $\theta$ of a cascade model from observed cascades. Similar lower
 bounds were obtained for sparse linear inverse problems in \cite{pw11, pw12,
@@ -7,7 +7,7 @@ bipw11}
 
 \begin{theorem}
     \label{thm:lb}
-    Let us consider a cascade model of the form XXX and a recovery algorithm
+    Let us consider a cascade model of the form \eqref{eq:glm} and a recovery algorithm
     $\mathcal{A}$ which takes as input $n$ random cascade measurements and
     outputs $\hat{\theta}$ such that with probability $\delta<\frac{1}{2}$
     (over the measurements):
@@ -19,7 +19,7 @@ bipw11}
     = \Omega(s\log\frac{m}{s}/\log C)$.
 \end{theorem}
 
-This theorem should be contrasted with Corollary~\ref{cor:relaxing}: up to an additive
+This theorem should be contrasted with Theorem~\ref{thm:approx_sparse}: up to an additive
 $s\log s$ factor, the number of measurements required by our algorithm is
 tight.