Moving the proofs to the appendix, improving the flow

1 files changed, 1 insertions, 1 deletions

diff --git a/intro.tex b/intro.tex
index 6002b46..0712540 100644
--- a/intro.tex
+++ b/intro.tex
@@ -68,7 +68,7 @@ From a technical perspective, we present a convex relaxation of \eqref{obj}, and
 %Our approach to mechanisms for experimental design --- by 
 % optimizing  the information gain in parameters like $\beta$ which are estimated through the data analysis process --- is general.  We give examples of this approach beyond linear regression to a general class that includes logistic regression and learning binary functions, and show that the corresponding budgeted mechanism design problem is also expressed through a submodular optimization.  Hence,  prior work \cite{chen,singer-mechanisms} immediately applies, and gives randomized, universally truthful, polynomial time, constant factor approximation mechanisms for problems in this class. Getting deterministic, truthful, polynomial time mechanisms with a constant approximation factor for this class or specific problems in it, like we did for \EDP, remains an open problem.
 
-In what follows, we describe related work in Section~\ref{sec:related}. We briefly review  experimental design and budget feasible mechanisms in Section~\ref{sec:peel} and define \SEDP\ formally. In Section~\ref{sec:main} we present our mechanism for \SEDP\ and state our main results,  which are proved in Section~\ref{sec:proofs}. A generalization of our framework to machine learning tasks beyond linear regression is presented in Section~\ref{sec:ext}. 
+In what follows, we describe related work in Section~\ref{sec:related}. We briefly review  experimental design and budget feasible mechanisms in Section~\ref{sec:peel} and define \SEDP\ formally. In Section~\ref{sec:main} we present our mechanism for \SEDP\ and state our main results. A generalization of our framework to machine learning tasks beyond linear regression is presented in Section~\ref{sec:ext}. 
 
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