Add sample file

sample.tex

@@ -0,0 +1,134 @@
+\documentclass[twoside,11pt]{article}
+
+% Any additional packages needed should be included after jmlr2e.
+% Note that jmlr2e.sty includes epsfig, amssymb, natbib and graphicx,
+% and defines many common macros, such as 'proof' and 'example'.
+%
+% It also sets the bibliographystyle to plainnat; for more information on
+% natbib citation styles, see the natbib documentation, a copy of which
+% is archived at https://www.jmlr.org/format/natbib.pdf
+
+\usepackage{jmlr2e}
+
+% Definitions of handy macros can go here
+
+\newcommand{\dataset}{{\cal D}}
+\newcommand{\fracpartial}[2]{\frac{\partial #1}{\partial #2}}
+
+% Heading arguments are {volume}{year}{pages}{date submitted}{date published}{paper id}{author-full-names}
+
+\jmlrheading{1}{2000}{1-48}{4/00}{10/00}{meila00a}{Marina Meil\u{a} and Michael I. Jordan}
+
+% Short headings should be running head and authors last names
+
+\ShortHeadings{Learning with Mixtures of Trees}{Meil\u{a} and Jordan}
+\firstpageno{1}
+
+\begin{document}
+
+\title{Learning with Mixtures of Trees}
+
+\author{\name Marina Meil\u{a} \email [email protected] \\
+ \addr Department of Statistics\\
+ University of Washington\\
+ Seattle, WA 98195-4322, USA
+ \AND
+ \name Michael I.\ Jordan \email [email protected] \\
+ \addr Division of Computer Science and Department of Statistics\\
+ University of California\\
+ Berkeley, CA 94720-1776, USA}
+
+\editor{Kevin Murphy and Bernhard Sch{\"o}lkopf}
+
+\maketitle
+
+\begin{abstract}% <- trailing '%' for backward compatibility of .sty file
+This paper describes the mixtures-of-trees model, a probabilistic 
+model for discrete multidimensional domains. Mixtures-of-trees 
+generalize the probabilistic trees of \citet{chow:68}
+in a different and complementary direction to that of Bayesian networks.
+We present efficient algorithms for learning mixtures-of-trees 
+models in maximum likelihood and Bayesian frameworks. 
+We also discuss additional efficiencies that can be
+obtained when data are ``sparse,'' and we present data 
+structures and algorithms that exploit such sparseness.
+Experimental results demonstrate the performance of the 
+model for both density estimation and classification. 
+We also discuss the sense in which tree-based classifiers
+perform an implicit form of feature selection, and demonstrate
+a resulting insensitivity to irrelevant attributes.
+\end{abstract}
+
+\begin{keywords}
+ Bayesian Networks, Mixture Models, Chow-Liu Trees
+\end{keywords}
+
+\section{Introduction}
+
+Probabilistic inference has become a core technology in AI,
+largely due to developments in graph-theoretic methods for the 
+representation and manipulation of complex probability 
+distributions~\citep{pearl:88}. Whether in their guise as 
+directed graphs (Bayesian networks) or as undirected graphs (Markov 
+random fields), \emph{probabilistic graphical models} have a number 
+of virtues as representations of uncertainty and as inference engines. 
+Graphical models allow a separation between qualitative, structural
+aspects of uncertain knowledge and the quantitative, parametric aspects 
+of uncertainty...\\
+
+{\noindent \em Remainder omitted in this sample. See https://www.jmlr.org/papers/ for full paper.}
+
+% Acknowledgements should go at the end, before appendices and references
+
+\acks{We would like to acknowledge support for this project
+from the National Science Foundation (NSF grant IIS-9988642)
+and the Multidisciplinary Research Program of the Department
+of Defense (MURI N00014-00-1-0637). }
+
+% Manual newpage inserted to improve layout of sample file - not
+% needed in general before appendices/bibliography.
+
+\newpage
+
+\appendix
+\section*{Appendix A.}
+\label{app:theorem}
+
+% Note: in this sample, the section number is hard-coded in. Following
+% proper LaTeX conventions, it should properly be coded as a reference:
+
+%In this appendix we prove the following theorem from
+%Section~\ref{sec:textree-generalization}:
+
+In this appendix we prove the following theorem from
+Section~6.2:
+
+\noindent
+{\bf Theorem} {\it Let $u,v,w$ be discrete variables such that $v, w$ do
+not co-occur with $u$ (i.e., $u\neq0\;\Rightarrow \;v=w=0$ in a given
+dataset $\dataset$). Let $N_{v0},N_{w0}$ be the number of data points for
+which $v=0, w=0$ respectively, and let $I_{uv},I_{uw}$ be the
+respective empirical mutual information values based on the sample
+$\dataset$. Then
+\[
+ N_{v0} \;>\; N_{w0}\;\;\Rightarrow\;\;I_{uv} \;\leq\;I_{uw}
+\]
+with equality only if $u$ is identically 0.} \hfill\BlackBox
+
+\noindent
+{\bf Proof}. We use the notation:
+\[
+P_v(i) \;=\;\frac{N_v^i}{N},\;\;\;i \neq 0;\;\;\;
+P_{v0}\;\equiv\;P_v(0)\; = \;1 - \sum_{i\neq 0}P_v(i).
+\]
+These values represent the (empirical) probabilities of $v$
+taking value $i\neq 0$ and 0 respectively. Entropies will be denoted
+by $H$. We aim to show that $\fracpartial{I_{uv}}{P_{v0}} < 0$....\\
+
+{\noindent \em Remainder omitted in this sample. See https://www.jmlr.org/papers/ for full paper.}
+
+
+\vskip 0.2in
+\bibliography{sample}
+
+\end{document}