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 \subsection{D-Optimality Criterion}
-Ideally, motivated by the $D$-optimality criterion, we would like to design a mechanism that maximizes \eqref{dcrit} within a good approximation ratio. As \eqref{dcrit} may take arbitrarily small negative values, to define a meaningful approximation we consider  the (equivalent) maximization of $V(S) = f(\det\T{X_S}X_S )$, for some increasing, on-to function $f:\reals_+\to\reals_+$. However, the following lower bound implies that such an optimization goal cannot be attained under the costraints of  truthfulness, budget feasibility, and individional rationallity.
+Ideally, motivated by the $D$-optimality criterion, we would like to design a mechanism that maximizes \eqref{dcrit} within a good approximation ratio. As \eqref{dcrit} may take arbitrarily small negative values, to define a meaningful approximation we consider  the (equivalent) maximization of $V(S) = f(\det\T{X_S}X_S )$, for some strictly increasing, on-to function $f:\reals_+\to\reals_+$. However, the following lower bound implies that such an optimization goal cannot be attained under the costraints of  truthfulness, budget feasibility, and individional rationallity.
 
 \begin{lemma}
-For any $M>1$, there is no truthful, budget feasible, individionally rational mechanism for optimal mechanism design with value fuction $V(S) = \det{\T{X_S}X_S}$.
+For any $M>1$, there is no $M$-approximate, truthful, budget feasible, individionally rational mechanism for budget feasible experiment design with value fuction $V(S) = \det{\T{X_S}X_S}$.
 \end{lemma}
 \begin{proof}
 \input{proof_of_lower_bound1}
 \end{proof}
 
-This negative result motivates us to look at the following modified objective:
+This negative result motivates us into looking at the following modified objective:
 \begin{align}V(S) = \log\det(I_d+\T{X_S}X_S), \label{modified}\end{align} where $I_d\in \reals^{d\times d}$ is the identity matrix.
 One possible interpretation of \eqref{modified} is that, prior to the auction, the experimenter has free access to $d$ experiments whose features form an ortho-normal basis in $\reals^d$. However, \eqref{modified} can also be motivated in the context of \emph{Bayesian experimental design} \cite{chaloner1995bayesian}. In short, the objective \eqref{modified} arises naturally when the experimenter retrieves the model $\beta$ through \emph{ridge regression}, rather than the linear regression \eqref{leastsquares} over the observed data; we explore this connection in Section~\ref{sec:bed}. From a practical standpoint, \eqref{modified} is a good approximation of \eqref{dcrit} when the number of experiments is large. Crucially, \eqref{modified} is submodular and satifies $V(\emptyset) = 0$, allowing us to use the extensive machinery for the optimization of submodular functions, as well as recent results in the context of budget feasible auctions.