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| -rw-r--r-- | abstract.tex | 2 |
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diff --git a/abstract.tex b/abstract.tex index 5aa4bdb..614a36b 100644 --- a/abstract.tex +++ b/abstract.tex @@ -19,6 +19,6 @@ Each subject $i$ declares an associated cost $c_i >0$ to be part of the experime mechanism for \SEDP{} with suitable properties. We present a deterministic, polynomial time, budget feasible mechanism scheme, that is approximately truthful and yields a constant factor approximation to \EDP. In particular, for any small $\delta>0$ and $\varepsilon>0$, we can construct a $(12.98\,,\varepsilon)$-approximate mechanism that is $\delta$-truthful and runs in polynomial time in both $n$ and $\log\log\frac{B}{\epsilon\delta}$. -By applying previous work on budget feasible mechanisms with a submodular objective, one could {\em only} have derived either an exponential time deterministic mechanism or a randomized polynomial time mechanism. We also establish that no truthful, budget-feasible algorithms are possible within a factor $2$ approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression. +By applying previous work on budget feasible mechanisms with a submodular objective, one could {\em only} have derived either an exponential time deterministic mechanism or a randomized polynomial time mechanism. We also establish that no truthful, budget-feasible mechanism is possible within a factor $2$ approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression. |