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+\documentclass{acm_proc_article-sp}
+\usepackage[utf8]{inputenc}
+\usepackage{amsmath,amsfonts}
+\usepackage{algorithm}
+\usepackage{algpseudocode}
+\newtheorem{lemma}{Lemma}
+\newtheorem{fact}{Fact}
+\newtheorem{example}{Example}
+\newtheorem{prop}{Proposition}
+\newtheorem{theorem}{Theorem}
+\newcommand*{\defeq}{\stackrel{\text{def}}{=}}
+\newcommand{\var}{\mathop{\mathrm{Var}}}
+\newcommand{\condexp}[2]{\mathop{\mathbb{E}}\left[#1|#2\right]}
+\newcommand{\expt}[1]{\mathop{\mathbb{E}}\left[#1\right]}
+\newcommand{\norm}[1]{\lVert#1\rVert}
+\newcommand{\tr}[1]{#1^*}
+\newcommand{\ip}[2]{\langle #1, #2 \rangle}
+\newcommand{\mse}{\mathop{\mathrm{MSE}}}
+\DeclareMathOperator{\trace}{tr}
+\DeclareMathOperator*{\argmax}{arg\,max}
+\title{Budgeted mechanism for optimal experiment design}
+\begin{document}
+
+\section{Intro}
+
+\begin{itemize}
+    \item already existing field of experiment design: survey-like setup, what
+    are the best points to include in your experiment. Measure of the
+    usefulness of the data: variance-reduction or entropy-reduction
+    \item nowadays, there is also a big focus on purchasing data: paid surveys,
+    mechanical turk, etc. that add economic aspects to the problem of
+    experiment desgin
+    \item recent advances (Singer, Chen) in the field of budgeted mechanisms
+    \item we study ridge regression, very widely used in statistical learning,
+    and treat it as a problem of budgeted experiment design
+    \item we make the following contributions: ...
+    \item extension to a more general setup which includes a wider class of
+    machine learning problems
+\end{itemize}
+
+\section{Problem formulation}
+
+\section{Main result}
+
+\section{General setup}
+
+\section{Conclusions}
+\end{document}