small

1 files changed, 3 insertions, 3 deletions

diff --git a/conclusion.tex b/conclusion.tex
index 5d32a1e..6a3917e 100644
--- a/conclusion.tex
+++ b/conclusion.tex
@@ -1,12 +1,12 @@
-We have proposed a convex relaxation for \EDP, and showed that it can be used to design a $\delta$-truthful, constant approximation mechanism that runs in polynomial time. Our objective function, commonly known as the Bayes $D$-optimality criterion, is motivated from linear regression, and in particular captures the information gain when experiments are used to learn a linear model. %in \reals^d.
+We have proposed a convex relaxation for \EDP, and showed that it can be used to design a $\delta$-truthful, constant approximation mechanism that runs in polynomial time. Our objective function, commonly known as the Bayes $D$-optimality criterion, is motivated by linear regression, and in particular captures the information gain when experiments are used to learn a linear model. %in \reals^d.
 
 A natural question to ask is to what extent the results we present here
 generalize to other machine learning tasks beyond linear regression. We outline
 a path in pursuing such generalizations in Appendix~\ref{sec:ext}. In
 particular, although the information gain is not generally a submodular
 function, we show that for a wide class of models, in which experiments
-outcomes are perturbed by independent noise, the information does indeed
-exhibit submodularity. Several important  learning tasks fall under this
+outcomes are perturbed by independent noise, the information gain indeed
+exhibits submodularity. Several important  learning tasks fall under this
 category, including generalized linear regression, logistic regression,
 \emph{etc.} In light of this, it would be interesting to investigate whether
 our convex relaxation approach generalizes to other learning tasks in this