intro

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diff --git a/related.tex b/related.tex
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@@ -18,7 +18,7 @@ McSherry and Talwar \cite{mcsherrytalwar} argue that \emph{differentially privat
 simultaneously achieve exact truthfulness as well as differential privacy. Eliciting private data through a \emph{survey} \cite{roth-liggett}, whereby individuals first decide whether to participate in the survey and then report their data,
  also fall under the unverified database setting \cite{xiao:privacy-truthfulness}. In the \emph{verified} database setting, Ghosh and Roth~\cite{ghosh-roth:privacy-auction} and Dandekar \emph{et al.}~\cite{pranav} consider budgeted auctions where users have a utility again captured by differential privacy. Our work departs from the above setups in that utilities do not involve privacy, whose effects are assumed to be internalized in the costs reported by the users; crucially, we also assume that experiments are tamper-proof, and individuals can misreport their costs but not their values.
 
-Our work is closest to the survey setup of Roth and Schoenebeck~\cite{roth-schoenebeck}, who also consider how to sample individuals with different features who reported a hidden value at a certain cost. The authors assume a prior on the joint distribution between costs and features, and wish to obtain an unbiased estimate of the expectation of the hidden value under the constraints of truthfulness, budget feasibility and individual rationality. Our work departs by learning a more general statistic (a linear model) than data means. We note that, as in \cite{roth-shoenebeck}, costs and features can be arbitrarily corellated (our results are prior-free).
+Our work is closest to the survey setup of Roth and Schoenebeck~\cite{roth-schoenebeck}, who also consider how to sample individuals with different features who reported a hidden value at a certain cost. The authors assume a prior on the joint distribution between costs and features, and wish to obtain an unbiased estimate of the expectation of the hidden value under the constraints of truthfulness, budget feasibility and individual rationality. Our work departs by learning a more general statistic (a linear model) than data means. We note that, as in \cite{roth-schoenebeck}, costs and features can be arbitrarily corellated (our results are prior-free).
 
 
 %\stratis{TODO: privacy discussion. Logdet objective. Should be one paragraph each.}