From c464d2fc6bdc81f9fa28c868fd1fca1987e6fa5b Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Mon, 11 Feb 2013 17:19:34 -0800
Subject: math

---
 abstract.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'abstract.tex')

diff --git a/abstract.tex b/abstract.tex
index eebeb97..a92ad45 100644
--- a/abstract.tex
+++ b/abstract.tex
@@ -18,7 +18,7 @@ We initiate the study of budgeted mechanisms for experimental design. In this se
 Each subject $i$ declares associated cost $c_i >0$ to be part of the experiment, and must be paid at least their cost.  Further,  the subjects 
 are \emph{strategic} and may lie about their costs . In particular, we formulate the {\em Strategic Experimental Design Problem} (\SEDP) as finding a set $S$ of subjects for the experiment that maximizes $V(S) = \log\det(I_d+\sum_{i\in S}x_i\T{x_i})$ under the constraint $\sum_{i\in S}c_i\leq B$; our objective function corresponds to  the information gain in  parameter  $\beta$ that is learned through linear regression methods, and is related to the so-called $D$-optimality criterion. 
 
-We present a deterministic, polynomial time, truthful, budget feasible mechanism for \EDP{}.
+We present a deterministic, polynomial time, truthful, budget feasible mechanism for \SEDP{}.
 By applying previous work on budget feasible mechanisms with submodular objective, one could have derived either an exponential time deterministic mechanism or a randomized  polynomial time mechanism. Our mechanism yields a constant factor ($\approx 12.68$) approximation, and we show that no truthful, budget-feasible algorithms are possible within a factor $2$ approximation.  We also show how to apply our approach to a wide class of learning problems. 
 
 
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