From ea386112229740787e940788c4f821c779732920 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Mon, 5 Nov 2012 12:32:11 -0800
Subject: intro abstract

---
 intro.tex | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index 83d0f87..e377462 100644
--- a/intro.tex
+++ b/intro.tex
@@ -6,7 +6,7 @@ known to the experimenter.
 Typically, \E\ has a hypothesis of the relationship between $x_i$'s and $y_i$'s, such as, say linear, \emph{i.e.},  $y_i \approx  \T{\beta} x_i$; conducting the experiments and obtaining the measurements $y_i$ lets \E\  estimate  $\beta$. %The goal of experimental design amounts to determining which subjects to experiment upon to produce the best possible such estimate.
  
 The above experimental design scenario  has many applications, from medical testing to marketing research and others. 
-There is a rich literature about estimation procedures, as well as for means for quantifying the quality of the produced estimate  \cite{pukelsheim2006optimal}.  There is also an extensive theory on how to select subjects 
+There is a rich literature about estimation procedures, as well as for means of quantifying the quality of the produced estimate  \cite{pukelsheim2006optimal}.  There is also an extensive theory on how to select subjects 
 if \E\ can conduct only a limited number of experiments, so the estimation process returns $\beta$
 that approximates the true parameter of the underlying population \cite{ginebra2007measure,le1996comparison,chaloner1995bayesian,boyd2004convex}. 
 
@@ -38,14 +38,14 @@ For specific combinatorial problems such as \textsc{Knapsack} or \textsc{Coverag
 deterministic, truthful, polynomial constant-approximation algorithms~\cite{singer-mechanisms,chen, singer-influence}, but they do not work for the linear-algebraic  objective function in \EDP.
 %{\bf S+T: could we verify that the above sentence is correct in its implication?}
 
-We present the first known, polynomial time truthful mechanism for \EDP{}. Our mechanism is a constant factor ($\approx 19.68$) approximation for \EDP{}. From a technical perspective, we present a convex relaxation to \EDP\; and show that it is within a constant factor approximation of the multi-linear relaxation for \EDP{}. This allows us to adopt the prior work in budget feasible mechanisms by Chen \emph{et al.}~\cite{chen} and Singer~\cite{singer-influence}.  In contrast to this, we show that no truthful, budget-feasible algorithms are possible for \EDP{}  within a factor 2 approximation. %{\bf FIX the last sentence}
+We present the first known, polynomial time truthful mechanism for \EDP{}. Our mechanism is a constant factor ($\approx 19.68$) approximation for \EDP{}. In contrast to this, we show that no truthful, budget-feasible algorithms are possible for \EDP{}  within a factor 2 approximation. From a technical perspective, we present a convex relaxation of \eqref{obj}, and show that it is within a constant factor from the so-called multi-linear relaxation of  \eqref{obj}. This allows us to adopt the approach followed by prior work in budget feasible mechanisms by Chen \emph{et al.}~\cite{chen} and Singer~\cite{singer-influence}.   %{\bf FIX the last sentence}
 
 \item
 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, the prior work \cite{chen,singer-mechanisms} immediately applies, and gives universally truthful, polynomial timem constant factor approximation mechanisms for problems in this class. Getting deterministic truthful polynomial time mechanisms for this class or specific problems in it, like we did for \EDP, remains an open problem.
+ 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.
 \end{itemize}
 
-In what follows, we described related work in Section~\ref{sec:related}. We describe the basics of experimental design and budget feasible mechanisms in Section~\ref{sec:peel} and define \EDP\ formally. In Section~\ref{sec:main} we present our mechanism for \EDP\ and prove our main results. In~\ref{sec:ext} we present applications of out general framework. 
+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 \EDP\ formally. In Section~\ref{sec:main} we present our mechanism for \EDP\ and prove our main results. The present applications of our general framework are presented in Section~\ref{sec:ext}. 
 
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