From 7f6b960702e38cf2e34d5594ba2b37a45f8a9520 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Thu, 12 Dec 2013 17:48:03 -0500
Subject: Reimport things from the appendix to the main part for the
 camera-ready version

---
 intro.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index da38f8f..8e684a0 100755
--- a/intro.tex
+++ b/intro.tex
@@ -61,7 +61,7 @@ Our convex relaxation of \EDP{} involves maximizing a self-concordant function s
 %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. We present our convex relaxation to \EDP{} in Section~\ref{sec:approximation} and use it to construct our mechanism in Section~\ref{sec:main}. We conclude in Section~\ref{sec:concl}. All proofs of our technical results are provided in the appendix. %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}. 
+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. We present our convex relaxation to \EDP{} in Section~\ref{sec:approximation} and use it to construct our mechanism in Section~\ref{sec:main}. We conclude in Section~\ref{sec:concl}. All proofs of our technical results are provided in the full version of this paper \cite{arxiv}. %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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