intro related

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@@ -41,7 +41,7 @@ subject to a budget constraint $\sum_{i\in S}c_i\leq B$, where $B$ is \E's budge
 \smallskip
 The objective function, which is the key, is formally obtained by optimizing  the information gain in  $\beta$ when the latter is learned  through ridge regression, and is related to  the so-called $D$-optimality criterion~\cite{pukelsheim2006optimal,atkinson2007optimum}. 
 \item
-We present a polynomial time, $\epsilon$-truthful mechanism for \SEDP{}, yielding a constant factor ($\approx 12.98$) approximation to the optimal value of \eqref{obj}. In contrast to this, we show that no truthful, budget-feasible mechanisms are possible for \SEDP{}  within a factor 2 approximation. 
+We present a polynomial time, $\delta$-truthful mechanism for \SEDP{}, yielding a constant factor ($\approx 12.98$) approximation to the optimal value of \eqref{obj}. In contrast to this, we show that no truthful, budget-feasible mechanisms are possible for \SEDP{}  within a factor 2 approximation. 
 
 \smallskip
 We note that the objective \eqref{obj} is submodular. Using this fact, applying previous results on budget feasible mechanism design under general submodular objectives~\cite{singer-mechanisms,chen} would yield either a deterministic, truthful, constant-approximation mechanism that requires exponential time,  or a non-deterministic, (universally) truthful, poly-time mechanism that yields a constant approximation ratio only \emph{in expectation} (\emph{i.e.}, its approximation guarantee for a given instance may in fact be unbounded).
@@ -59,7 +59,12 @@ We note that the objective \eqref{obj} is submodular. Using this fact, applying
 %Though such mechanisms were known to exist for  combinatorial problems with specific submodular objectives such as \textsc{Knapsack} or \textsc{Coverage}~\cite{singer-mechanisms,chen, singer-influence}, these do not readily apply to the more complicated linear-algebraic  objective function \eqref{obj} of  \SEDP.
 %{\bf S+T: could we verify that the above sentence is correct in its implication?}
 
-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}, which in turn can be related to \eqref{obj} through pipage rounding. We establish the constant factor to the multi-linear relaxation by bounding  the partial derivatives of these two functions; we achieve the latter by exploiting convexity properties of matrix functions over the convex cone of positive semidefinite matrices.
+%From a technical perspective, we present a convex relaxation of \eqref{obj}, and show that its optimal value is within a constant factor from the so-called multi-linear relaxation of  \eqref{obj}, which in turn can be related to \eqref{obj} through pipage rounding. We establish the constant factor to the multi-linear relaxation by bounding  the partial derivatives of these two functions; we achieve the latter by exploiting convexity properties of matrix functions over the convex cone of positive semidefinite matrices.
+
+From a technical perspective, we propose a convex optimization problem and establish that its optimal value within a constant factor from the optimal value of \EDP.  
+ In particular, we show our relaxed objective is within a constant factor from the so-called multi-linear extension of  \eqref{obj}, which in turn can be related to \eqref{obj} through pipage rounding. We establish the constant factor to the multi-linear extension by bounding  the partial derivatives of these two functions; we achieve the latter by exploiting convexity properties of matrix functions over the convex cone of positive semidefinite matrices.
+
+Our convex relaxation of \EDP{} involves maximizing a self-concordant function subject to linear constraints. Its optimal value can be computed with arbitrary accuracy in polynomial time using the so-called barrier method. However, the outcome of this computation may not nevessarily be monotone, a property needed in designing a truthful mechanism. Nevetheless, we construct an algorithm that solves the above convex relaxation and is ``almost'' monotone; in turn, we also show that this algorithm can be employed to design a $\delta$-truthful mechanism for \EDP{}.
 
 
 %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}
@@ -68,7 +73,7 @@ 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. 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, finally, show how it can be used to construct our mechanism 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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