From 813a7f621532c8915da679df81a17aea73059f1f Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Thu, 31 Oct 2013 19:04:30 -0400
Subject: Wine abstract

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+\documentclass{llncs}
+\usepackage[numbers]{natbib}
+\usepackage[utf8x]{inputenc}
+\usepackage{amsmath,amsfonts}
+\usepackage{algorithm, algpseudocode}
+\usepackage{bbm,color,verbatim}
+\input{definitions}
+\usepackage[pagebackref=true,breaklinks=true,colorlinks=true]{hyperref}
+\title{Budget Feasible Mechanisms\\ for Experimental Design}
+\author{
+    Thibaut Horel\inst{1}
+    \and
+    Stratis Ioannidis\inst{2}
+    \and
+    S. Muthukrishnan\inst{3}
+}
+\institute{École Normale Supérieure, \email{thibaut.horel@normalesup.org}
+    \and
+    Technicolor, \email{stratis.ioannidis@technicolor.com}
+    \and
+    Rutgers University, \email{muthu@cs.rutgers.edu}
+}
+\begin{document}
+\maketitle
+\vspace{2em}
+
+In the classical {\em experimental design} setting, an  experimenter  \E\ has
+access to a population of $n$ potential experiment subjects $i\in
+\{1,\ldots,n\}$,  each associated with a vector of features $x_i\in\reals^d$.
+Conducting an experiment with subject $i$  reveals an unknown value $y_i\in
+\reals$ to \E. \E\ typically assumes some hypothetical relationship between
+$x_i$'s and $y_i$'s, \emph{e.g.},  $y_i \approx  \T{\beta} x_i$, and estimates
+$\beta$ from experiments, \emph{e.g.}, through linear regression.  As a proxy
+for various practical constraints, \E{} may select only a subset of subjects on
+which to conduct the experiment. 
+
+We initiate the study of budgeted mechanisms for experimental design. In this
+setting,  \E{} has a budget $B$.  Each subject $i$ declares an associated cost
+$c_i >0$ to be part of the experiment, and must be paid at least her cost.  In
+particular,  the {\em  Experimental Design Problem} (\SEDP) is to find 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.  Further,  the subjects  are
+\emph{strategic} and may lie about their costs. Thus, we need to design
+a mechanism for \SEDP{} with suitable properties.  We present a deterministic,
+polynomial time, budget feasible mechanism scheme, that is approximately
+truthful and yields a 12.98 factor  approximation to \EDP.
+% 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.
+\end{document}
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